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Bae J, Tan Z, Solomon E, Huang Z, Heacock L, Moy L, Knoll F, Kim SG. Digital reference object toolkit of breast DCE MRI for quantitative evaluation of image reconstruction and analysis methods. Magn Reson Med 2024; 92:1728-1742. [PMID: 38775077 DOI: 10.1002/mrm.30152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/21/2024] [Accepted: 04/22/2024] [Indexed: 07/23/2024]
Abstract
PURPOSE To develop a digital reference object (DRO) toolkit to generate realistic breast DCE-MRI data for quantitative assessment of image reconstruction and data analysis methods. METHODS A simulation framework in a form of DRO toolkit has been developed using the ultrafast and conventional breast DCE-MRI data of 53 women with malignant (n = 25) or benign (n = 28) lesions. We segmented five anatomical regions and performed pharmacokinetic analysis to determine the ranges of pharmacokinetic parameters for each segmented region. A database of the segmentations and their pharmacokinetic parameters is included in the DRO toolkit that can generate a large number of realistic breast DCE-MRI data. We provide two potential examples for our DRO toolkit: assessing the accuracy of an image reconstruction method using undersampled simulated radial k-space data and assessing the impact of theB 1 + $$ {\mathrm{B}}_1^{+} $$ field inhomogeneity on estimated parameters. RESULTS The estimated pharmacokinetic parameters for each region showed agreement with previously reported values. For the assessment of the reconstruction method, it was found that the temporal regularization resulted in significant underestimation of estimated parameters by up to 57% and 10% with the weighting factor λ = 0.1 and 0.01, respectively. We also demonstrated that spatial discrepancy ofv p $$ {v}_p $$ andPS $$ \mathrm{PS} $$ increase to about 33% and 51% without correction forB 1 + $$ {\mathrm{B}}_1^{+} $$ field. CONCLUSION We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE-MRI reconstruction and analysis methods.
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Affiliation(s)
- Jonghyun Bae
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine, New York, New York, USA
- Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine, New York, New York, USA
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Zhengguo Tan
- Biomedical Engineering, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Erlangen, Germany
| | - Eddy Solomon
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Zhengnan Huang
- Vilcek Institute of Graduate Biomedical Science, New York University School of Medicine, New York, New York, USA
- Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine, New York, New York, USA
| | - Laura Heacock
- Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine, New York, New York, USA
| | - Linda Moy
- Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research, Radiology, New York University School of Medicine, New York, New York, USA
| | - Florian Knoll
- Biomedical Engineering, Friedrich-Alexander-Universitat Erlangen-Nurnberg, Erlangen, Germany
| | - Sungheon Gene Kim
- Department of Radiology, Weill Cornell Medical College, New York, New York, USA
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Zhang M, Ding B, Dragonu I, Liebig P, Rodgers CT. Dynamic parallel transmit diffusion MRI at 7T. Magn Reson Imaging 2024; 111:35-46. [PMID: 38547935 DOI: 10.1016/j.mri.2024.03.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 03/14/2024] [Accepted: 03/25/2024] [Indexed: 04/14/2024]
Abstract
Diffusion MRI (dMRI) is inherently limited by SNR. Scanning at 7 T increases intrinsic SNR but 7 T MRI scans suffer from regions of signal dropout, especially in the temporal lobes and cerebellum. We applied dynamic parallel transmit (pTx) to allow whole-brain 7 T dMRI and compared with circularly polarized (CP) pulses in 6 subjects. Subject-specific 2-spoke dynamic pTx pulses were designed offline for 8 slabs covering the brain. We used vendor-provided B0 and B1+ mapping. Spokes positions were set using the Fourier difference approach, and RF coefficients optimized with a Jacobi-matrix high-flip-angle optimizer. Diffusion data were analyzed with FSL. Comparing whole-brain averages for pTx against CP scans: mean flip angle error improved by 15% for excitation (2-spoke-VERSE 15.7° vs CP 18.4°, P = 0.012) and improved by 14% for refocusing (2-spoke-VERSE 39.7° vs CP 46.2°, P = 0.008). Computed spin-echo signal standard deviation improved by 14% (2-spoke-VERSE 0.185 vs 0.214 CP, P = 0.025). Temporal SNR increased by 5.4% (2-spoke-VERSE 8.47 vs CP 8.04, P = 0.004) especially in the inferior temporal lobes. Diffusion fitting uncertainty decreased by 6.2% for first fibers (2-spoke VERSE 0.0655 vs CP 0.0703, P < 0.001) and 1.3% for second fibers (2-spoke VERSE 0.139 vs CP 0.141, P = 0.01). In conclusion, dynamic parallel transmit improves the uniformity of 7 T diffusion-weighted imaging. In future, less restrictive SAR limits for parallel transmit scans are expected to allow further improvements.
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Affiliation(s)
- Minghao Zhang
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, United Kingdom.
| | - Belinda Ding
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, United Kingdom; Siemens Healthcare Ltd, Frimley, United Kingdom
| | | | | | - Christopher T Rodgers
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, United Kingdom
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Tazwar M, Evia AM, Ridwan AR, Leurgans SE, Bennett DA, Schneider JA, Arfanakis K. Limbic-predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) is associated with abnormalities in white matter structural integrity and connectivity: An ex-vivo diffusion MRI and pathology investigation. Neurobiol Aging 2024; 140:81-92. [PMID: 38744041 PMCID: PMC11182335 DOI: 10.1016/j.neurobiolaging.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 04/04/2024] [Accepted: 04/04/2024] [Indexed: 05/16/2024]
Abstract
Limbic predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) is common in older adults and is associated with neurodegeneration, cognitive decline and dementia. In this MRI and pathology investigation we tested the hypothesis that LATE-NC is associated with abnormalities in white matter structural integrity and connectivity of a network of brain regions typically harboring TDP-43 inclusions in LATE, referred to here as the "LATE-NC network". Ex-vivo diffusion MRI and detailed neuropathological data were collected on 184 community-based older adults. Linear regression revealed an independent association of higher LATE-NC stage with lower diffusion anisotropy in a set of white matter connections forming a pattern of connectivity that is consistent with the stereotypical spread of this pathology in the brain. Graph theory analysis revealed an association of higher LATE-NC stage with weaker integration and segregation in the LATE-NC network. Abnormalities were significant in stage 3, suggesting that they are detectable in later stages of the disease. Finally, LATE-NC network abnormalities were associated with faster cognitive decline, specifically in episodic and semantic memory.
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Affiliation(s)
- Mahir Tazwar
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Arnold M Evia
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Abdur Raquib Ridwan
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Sue E Leurgans
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA; Department of Pathology, Rush University Medical Center, Chicago, IL, USA
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Diagnostic Radiology, Rush University Medical Center, Chicago, IL, USA.
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Jansen MG, Zwiers MP, Marques JP, Chan KS, Amelink JS, Altgassen M, Oosterman JM, Norris DG. The Advanced BRain Imaging on ageing and Memory (ABRIM) data collection: Study design, data processing, and rationale. PLoS One 2024; 19:e0306006. [PMID: 38905233 PMCID: PMC11192316 DOI: 10.1371/journal.pone.0306006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 06/07/2024] [Indexed: 06/23/2024] Open
Abstract
To understand the neurocognitive mechanisms that underlie heterogeneity in cognitive ageing, recent scientific efforts have led to a growing public availability of imaging cohort data. The Advanced BRain Imaging on ageing and Memory (ABRIM) project aims to add to these existing datasets by taking an adult lifespan approach to provide a cross-sectional, normative database with a particular focus on connectivity, myelinization and iron content of the brain in concurrence with cognitive functioning, mechanisms of reserve, and sleep-wake rhythms. ABRIM freely shares MRI and behavioural data from 295 participants between 18-80 years, stratified by age decade and sex (median age 52, IQR 36-66, 53.20% females). The ABRIM MRI collection consists of both the raw and pre-processed structural and functional MRI data to facilitate data usage among both expert and non-expert users. The ABRIM behavioural collection includes measures of cognitive functioning (i.e., global cognition, processing speed, executive functions, and memory), proxy measures of cognitive reserve (e.g., educational attainment, verbal intelligence, and occupational complexity), and various self-reported questionnaires (e.g., on depressive symptoms, pain, and the use of memory strategies in daily life and during a memory task). In a sub-sample (n = 120), we recorded sleep-wake rhythms using an actigraphy device (Actiwatch 2, Philips Respironics) for a period of 7 consecutive days. Here, we provide an in-depth description of our study protocol, pre-processing pipelines, and data availability. ABRIM provides a cross-sectional database on healthy participants throughout the adult lifespan, including numerous parameters relevant to improve our understanding of cognitive ageing. Therefore, ABRIM enables researchers to model the advanced imaging parameters and cognitive topologies as a function of age, identify the normal range of values of such parameters, and to further investigate the diverse mechanisms of reserve and resilience.
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Affiliation(s)
- Michelle G. Jansen
- Donders Centre for Cognition, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Marcel P. Zwiers
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jose P. Marques
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Kwok-Shing Chan
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jitse S. Amelink
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Radboud University, Nijmegen, the Netherlands
| | - Mareike Altgassen
- Department of Psychology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Joukje M. Oosterman
- Donders Centre for Cognition, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - David G. Norris
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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Tayebi M, Kwon E, Maller J, McGeown J, Scadeng M, Qiao M, Wang A, Nielsen P, Fernandez J, Holdsworth S, Shim V. Integration of diffusion tensor imaging parameters with mesh morphing for in-depth analysis of brain white matter fibre tracts. Brain Commun 2024; 6:fcae027. [PMID: 38638147 PMCID: PMC11024816 DOI: 10.1093/braincomms/fcae027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 10/06/2023] [Accepted: 02/07/2024] [Indexed: 04/20/2024] Open
Abstract
Averaging is commonly used for data reduction/aggregation to analyse high-dimensional MRI data, but this often leads to information loss. To address this issue, we developed a novel technique that integrates diffusion tensor metrics along the whole volume of the fibre bundle using a 3D mesh-morphing technique coupled with principal component analysis for delineating case and control groups. Brain diffusion tensor MRI scans of high school rugby union players (n = 30, age 16-18) were acquired on a 3 T MRI before and after the sports season. A non-contact sport athlete cohort with matching demographics (n = 12) was also scanned. The utility of the new method in detecting differences in diffusion tensor metrics of the right corticospinal tract between contact and non-contact sport athletes was explored. The first step was to run automated tractography on each subject's native space. A template model of the right corticospinal tract was generated and morphed into each subject's native shape and space, matching individual geometry and diffusion metric distributions with minimal information loss. The common dimension of the 20 480 diffusion metrics allowed further data aggregation using principal component analysis to cluster the case and control groups as well as visualization of diffusion metric statistics (mean, ±2 SD). Our approach of analysing the whole volume of white matter tracts led to a clear delineation between the rugby and control cohort, which was not possible with the traditional averaging method. Moreover, our approach accounts for the individual subject's variations in diffusion tensor metrics to visualize group differences in quantitative MR data. This approach may benefit future prediction models based on other quantitative MRI methods.
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Affiliation(s)
- Maryam Tayebi
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
| | - Eryn Kwon
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
| | | | - Josh McGeown
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
| | - Miriam Scadeng
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, 1023, New Zealand
| | - Miao Qiao
- Department of Computer Science, The University of Auckland, Auckland, 1010, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, 1023, New Zealand
| | - Poul Nielsen
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Department of Engineering Science, The University of Auckland, Auckland, 1010, New Zealand
| | - Justin Fernandez
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
- Department of Engineering Science, The University of Auckland, Auckland, 1010, New Zealand
| | - Samantha Holdsworth
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, 1023, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
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Davies-Jenkins CW, Döring A, Fasano F, Kleban E, Mueller L, Evans CJ, Afzali M, Jones DK, Ronen I, Branzoli F, Tax CMW. Practical considerations of diffusion-weighted MRS with ultra-strong diffusion gradients. Front Neurosci 2023; 17:1258408. [PMID: 38144210 PMCID: PMC10740196 DOI: 10.3389/fnins.2023.1258408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 11/03/2023] [Indexed: 12/26/2023] Open
Abstract
Introduction Diffusion-weighted magnetic resonance spectroscopy (DW-MRS) offers improved cellular specificity to microstructure-compared to water-based methods alone-but spatial resolution and SNR is severely reduced and slow-diffusing metabolites necessitate higher b-values to accurately characterize their diffusion properties. Ultra-strong gradients allow access to higher b-values per-unit time, higher SNR for a given b-value, and shorter diffusion times, but introduce additional challenges such as eddy-current artefacts, gradient non-uniformity, and mechanical vibrations. Methods In this work, we present initial DW-MRS data acquired on a 3T Siemens Connectom scanner equipped with ultra-strong (300 mT/m) gradients. We explore the practical issues associated with this manner of acquisition, the steps that may be taken to mitigate their impact on the data, and the potential benefits of ultra-strong gradients for DW-MRS. An in-house DW-PRESS sequence and data processing pipeline were developed to mitigate the impact of these confounds. The interaction of TE, b-value, and maximum gradient amplitude was investigated using simulations and pilot data, whereby maximum gradient amplitude was restricted. Furthermore, two DW-MRS voxels in grey and white matter were acquired using ultra-strong gradients and high b-values. Results Simulations suggest T2-based SNR gains that are experimentally confirmed. Ultra-strong gradient acquisitions exhibit similar artefact profiles to those of lower gradient amplitude, suggesting adequate performance of artefact mitigation strategies. Gradient field non-uniformity influenced ADC estimates by up to 4% when left uncorrected. ADC and Kurtosis estimates for tNAA, tCho, and tCr align with previously published literature. Discussion In conclusion, we successfully implemented acquisition and data processing strategies for ultra-strong gradient DW-MRS and results indicate that confounding effects of the strong gradient system can be ameliorated, while achieving shorter diffusion times and improved metabolite SNR.
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Affiliation(s)
- Christopher W. Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - André Döring
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- CIBM Center for Biomedical Imaging, EPFL CIBM-AIT, EPFL Lausanne, Lausanne, Switzerland
| | - Fabrizio Fasano
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Siemens Healthcare Ltd., Camberly, United Kingdom
| | - Elena Kleban
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Department of Radiology, Universität Bern, Bern, Switzerland
| | - Lars Mueller
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Leeds Institute of Cardiovascular & Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - C. John Evans
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
- Leeds Institute of Cardiovascular & Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom
| | - Itamar Ronen
- Clinical Sciences Institue, Brighton and Sussex Medical School, Brighton, United Kingdom
| | - Francesca Branzoli
- Center for NeuroImaging Research (CENIR), Paris Brain Institute (ICM), Pitié-Salpêtrière Hospital, Paris, France
- Inserm U1127, CNRS U7225, Sorbonne Universités, Paris, France
| | - Chantal M. W. Tax
- Brain Research Imaging Centre, School Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
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Weaver JM, DiPiero M, Rodrigues PG, Cordash H, Davidson RJ, Planalp EM, Dean DC. Automated motion artifact detection in early pediatric diffusion MRI using a convolutional neural network. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:10.1162/imag_a_00023. [PMID: 38344118 PMCID: PMC10854394 DOI: 10.1162/imag_a_00023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Diffusion MRI (dMRI) is a widely used method to investigate the microstructure of the brain. Quality control (QC) of dMRI data is an important processing step that is performed prior to analysis using models such as diffusion tensor imaging (DTI) or neurite orientation dispersion and density imaging (NODDI). When processing dMRI data from infants and young children, where intra-scan motion is common, the identification and removal of motion artifacts is of the utmost importance. Manual QC of dMRI data is (1) time-consuming due to the large number of diffusion directions, (2) expensive, and (3) prone to subjective errors and observer variability. Prior techniques for automated dMRI QC have mostly been limited to adults or school-age children. Here, we propose a deep learning-based motion artifact detection tool for dMRI data acquired from infants and toddlers. The proposed framework uses a simple three-dimensional convolutional neural network (3DCNN) trained and tested on an early pediatric dataset of 2,276 dMRI volumes from 121 exams acquired at 1 month and 24 months of age. An average classification accuracy of 95% was achieved following four-fold cross-validation. A second dataset with different acquisition parameters and ages ranging from 2-36 months (consisting of 2,349 dMRI volumes from 26 exams) was used to test network generalizability, achieving 98% classification accuracy. Finally, to demonstrate the importance of motion artifact volume removal in a dMRI processing pipeline, the dMRI data were fit to the DTI and NODDI models and the parameter maps were compared with and without motion artifact removal.
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Affiliation(s)
- Jayse Merle Weaver
- Department of Medical Physics, University of Wisconsin–Madison, Madison, WI, United States
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
| | - Marissa DiPiero
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin–Madison, Madison, WI, United States
| | | | - Hassan Cordash
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
| | - Richard J. Davidson
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, United States
- Center for Healthy Minds, University of Wisconsin–Madison, Madison WI, United States
- Department of Psychiatry, University of Wisconsin–Madison, Madison, WI, United States
| | - Elizabeth M. Planalp
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Department of Medicine, University of Wisconsin–Madison, Madison, WI, United States
| | - Douglas C. Dean
- Department of Medical Physics, University of Wisconsin–Madison, Madison, WI, United States
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Department of Pediatrics, University of Wisconsin–Madison, Madison, WI, United States
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Cottam NC, Bamfo T, Harrington MA, Charvet CJ, Hekmatyar K, Tulin N, Sun J. Cerebellar structural, astrocytic, and neuronal abnormalities in the SMNΔ7 mouse model of spinal muscular atrophy. Brain Pathol 2023; 33:e13162. [PMID: 37218083 PMCID: PMC10467044 DOI: 10.1111/bpa.13162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 04/18/2023] [Indexed: 05/24/2023] Open
Abstract
Spinalmuscular atrophy (SMA) is a neuromuscular disease that affects as many as 1 in 6000 individuals at birth, making it the leading genetic cause of infant mortality. A growing number of studies indicate that SMA is a multi-system disease. The cerebellum has received little attention even though it plays an important role in motor function and widespread pathology has been reported in the cerebella of SMA patients. In this study, we assessed SMA pathology in the cerebellum using structural and diffusion magnetic resonance imaging, immunohistochemistry, and electrophysiology with the SMNΔ7 mouse model. We found a significant disproportionate loss in cerebellar volume, decrease in afferent cerebellar tracts, selective lobule-specific degeneration of Purkinje cells, abnormal lobule foliation and astrocyte integrity, and a decrease in spontaneous firing of cerebellar output neurons in the SMA mice compared to controls. Our data suggest that defects in cerebellar structure and function due to decreased survival motor neuron (SMN) levels impair the functional cerebellar output affecting motor control, and that cerebellar pathology should be addressed to achieve comprehensive treatment and therapy for SMA patients.
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Affiliation(s)
- Nicholas C. Cottam
- Department of Biological SciencesDelaware State UniversityDoverDelawareUSA
| | - Tiffany Bamfo
- Department of Biological SciencesDelaware State UniversityDoverDelawareUSA
| | | | - Christine J. Charvet
- Delaware Center for Neuroscience ResearchDelaware State UniversityDoverDelawareUSA
- Department of Anatomy, Physiology and PharmacologyAuburn UniversityAuburnAlabamaUSA
- Department of PsychologyDelaware State UniversityDoverDEUnited States
| | - Khan Hekmatyar
- Center for Biomedical and Brain ImagingUniversity of DelawareNewarkDelawareUSA
- Bioimaging Research Center for Biomedical and Brain ImagingUniversity of GeorgiaAthensGeorgiaUSA
| | - Nikita Tulin
- Department of NeuroscienceTemple UniversityPhiladelphiaPennsylvaniaUSA
| | - Jianli Sun
- Department of Biological SciencesDelaware State UniversityDoverDelawareUSA
- Delaware Center for Neuroscience ResearchDelaware State UniversityDoverDelawareUSA
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Martín-Martín C, Planchuelo-Gómez Á, Guerrero ÁL, García-Azorín D, Tristán-Vega A, de Luis-García R, Aja-Fernández S. Viability of AMURA biomarkers from single-shell diffusion MRI in clinical studies. Front Neurosci 2023; 17:1106350. [PMID: 37234256 PMCID: PMC10208402 DOI: 10.3389/fnins.2023.1106350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 03/30/2023] [Indexed: 05/27/2023] Open
Abstract
Diffusion Tensor Imaging (DTI) is the most employed method to assess white matter properties using quantitative parameters derived from diffusion MRI, but it presents known limitations that restrict the evaluation of complex structures. The objective of this study was to validate the reliability and robustness of complementary diffusion measures extracted with a novel approach, Apparent Measures Using Reduced Acquisitions (AMURA), with a typical diffusion MRI acquisition from a clinical context in comparison with DTI with application to clinical studies. Fifty healthy controls, 51 episodic migraine and 56 chronic migraine patients underwent single-shell diffusion MRI. Four DTI-based and eight AMURA-based parameters were compared between groups with tract-based spatial statistics to establish reference results. On the other hand, following a region-based analysis, the measures were assessed for multiple subsamples with diverse reduced sample sizes and their stability was evaluated with the coefficient of quartile variation. To assess the discrimination power of the diffusion measures, we repeated the statistical comparisons with a region-based analysis employing reduced sample sizes with diverse subsets, decreasing 10 subjects per group for consecutive reductions, and using 5,001 different random subsamples. For each sample size, the stability of the diffusion descriptors was evaluated with the coefficient of quartile variation. AMURA measures showed a greater number of statistically significant differences in the reference comparisons between episodic migraine patients and controls compared to DTI. In contrast, a higher number of differences was found with DTI parameters compared to AMURA in the comparisons between both migraine groups. Regarding the assessments reducing the sample size, the AMURA parameters showed a more stable behavior than DTI, showing a lower decrease for each reduced sample size or a higher number of regions with significant differences. However, most AMURA parameters showed lower stability in relation to higher coefficient of quartile variation values than the DTI descriptors, although two AMURA measures showed similar values to DTI. For the synthetic signals, there were AMURA measures with similar quantification to DTI, while other showed similar behavior. These findings suggest that AMURA presents favorable characteristics to identify differences of specific microstructural properties between clinical groups in regions with complex fiber architecture and lower dependency on the sample size or assessing technique than DTI.
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Affiliation(s)
- Carmen Martín-Martín
- Laboratorio de Procesado de Imagen (LPI), Universidad de Valladolid, Valladolid, Spain
| | - Álvaro Planchuelo-Gómez
- Laboratorio de Procesado de Imagen (LPI), Universidad de Valladolid, Valladolid, Spain
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Ángel L. Guerrero
- Headache Unit, Department of Neurology, Hospital Clínico Universitario de Valladolid, Valladolid, Spain
- Department of Medicine, Universidad de Valladolid, Valladolid, Spain
| | - David García-Azorín
- Headache Unit, Department of Neurology, Hospital Clínico Universitario de Valladolid, Valladolid, Spain
| | - Antonio Tristán-Vega
- Laboratorio de Procesado de Imagen (LPI), Universidad de Valladolid, Valladolid, Spain
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10
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Figley CR, Uddin MN, Wong K, Kornelsen J, Puig J, Figley TD. Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure. Front Neurosci 2022; 15:799576. [PMID: 35095400 PMCID: PMC8795606 DOI: 10.3389/fnins.2021.799576] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 12/17/2021] [Indexed: 01/31/2023] Open
Abstract
Fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) are commonly used as MRI biomarkers of white matter microstructure in diffusion MRI studies of neurodevelopment, brain aging, and neurologic injury/disease. Some of the more frequent practices include performing voxel-wise or region-based analyses of these measures to cross-sectionally compare individuals or groups, longitudinally assess individuals or groups, and/or correlate with demographic, behavioral or clinical variables. However, it is now widely recognized that the majority of cerebral white matter voxels contain multiple fiber populations with different trajectories, which renders these metrics highly sensitive to the relative volume fractions of the various fiber populations, the microstructural integrity of each constituent fiber population, and the interaction between these factors. Many diffusion imaging experts are aware of these limitations and now generally avoid using FA, AD or RD (at least in isolation) to draw strong reverse inferences about white matter microstructure, but based on the continued application and interpretation of these metrics in the broader biomedical/neuroscience literature, it appears that this has perhaps not yet become common knowledge among diffusion imaging end-users. Therefore, this paper will briefly discuss the complex biophysical underpinnings of these measures in the context of crossing fibers, provide some intuitive “thought experiments” to highlight how conventional interpretations can lead to incorrect conclusions, and suggest that future studies refrain from using (over-interpreting) FA, AD, and RD values as standalone biomarkers of cerebral white matter microstructure.
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Affiliation(s)
- Chase R. Figley
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Health Sciences Centre, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
- Department of Physiology & Pathophysiology, University of Manitoba, Winnipeg, MB, Canada
- *Correspondence: Chase R. Figley,
| | - Md Nasir Uddin
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Department of Neurology, University of Rochester, Rochester, NY, United States
| | - Kaihim Wong
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
| | - Jennifer Kornelsen
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Health Sciences Centre, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
- Department of Physiology & Pathophysiology, University of Manitoba, Winnipeg, MB, Canada
| | - Josep Puig
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Health Sciences Centre, Winnipeg, MB, Canada
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr. Josep Trueta, Girona, Spain
| | - Teresa D. Figley
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Health Sciences Centre, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
- Department of Physiology & Pathophysiology, University of Manitoba, Winnipeg, MB, Canada
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11
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Chabran E, Mondino M, Noblet V, Degiorgis L, Loureiro de Sousa P, Blanc F. Microstructural changes in prodromal dementia with Lewy bodies compared to normal aging: multiparametric quantitative MRI evidences. Eur J Neurosci 2021; 55:611-623. [PMID: 34888964 DOI: 10.1111/ejn.15558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 11/08/2021] [Accepted: 11/20/2021] [Indexed: 11/29/2022]
Abstract
Dementia with Lewy bodies (DLB) patients show few significant macroscopic structural changes, especially at the early stages of the disease, making quantitative MRI especially interesting to explore more subtle changes that are not detectable by conventional volumetric techniques. Microstructural alterations have been reported in DLB at the dementia stage, but no study to date was conducted in prodromal patients. Here, quantitative MRI data were collected from 46 DLB prodromal patients and 20 healthy elderly subjects, who also underwent a detailed clinical examination including the Mayo Clinic Fluctuation Scale. We conducted voxel-wise between-group comparisons in diffusion tensor imaging (DTI) metrics and in R2* mapping, along with a multivariate analysis combining the two modalities. We highlighted multiple grey matter and white matter microstructural changes in DLB patients at the prodromal stage, compared to control subjects. Our multivariate analysis identified three distinct regional patterns of DTI and R2* changes (anterior, anteromedial, posterior) in DLB patients, that could reflect different neuropathological processes across brain regions. We also observed an association between R2* alterations in the thalamus, and the severity of fluctuations, in the DLB group. These preliminary findings are promising and require future investigations to better understand the biological underpinnings of microstructural alterations.
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Affiliation(s)
- Eléna Chabran
- ICube Laboratory UMR 7357 and FMTS (Fédération de Médecine Translationnelle de Strasbourg), IMIS team and IRIS plateform, University of Strasbourg and CNRS, Strasbourg, France
| | - Mary Mondino
- ICube Laboratory UMR 7357 and FMTS (Fédération de Médecine Translationnelle de Strasbourg), IMIS team and IRIS plateform, University of Strasbourg and CNRS, Strasbourg, France
| | - Vincent Noblet
- ICube Laboratory UMR 7357 and FMTS (Fédération de Médecine Translationnelle de Strasbourg), IMIS team and IRIS plateform, University of Strasbourg and CNRS, Strasbourg, France
| | - Laetitia Degiorgis
- ICube Laboratory UMR 7357 and FMTS (Fédération de Médecine Translationnelle de Strasbourg), IMIS team and IRIS plateform, University of Strasbourg and CNRS, Strasbourg, France
| | - Paulo Loureiro de Sousa
- ICube Laboratory UMR 7357 and FMTS (Fédération de Médecine Translationnelle de Strasbourg), IMIS team and IRIS plateform, University of Strasbourg and CNRS, Strasbourg, France
| | - Frédéric Blanc
- ICube Laboratory UMR 7357 and FMTS (Fédération de Médecine Translationnelle de Strasbourg), IMIS team and IRIS plateform, University of Strasbourg and CNRS, Strasbourg, France.,CM2R (Research and Resources Memory Centre), Geriatric Day Hospital and Neuropsychology Unit, Geriatrics Department, University Hospitals of Strasbourg, Strasbourg, France
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12
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Same Brain, Different Look?-The Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters. J Clin Med 2021; 10:jcm10214987. [PMID: 34768507 PMCID: PMC8584364 DOI: 10.3390/jcm10214987] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/21/2021] [Accepted: 10/23/2021] [Indexed: 11/17/2022] Open
Abstract
In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19–54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.
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13
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Kurokawa R, Kamiya K, Koike S, Nakaya M, Uematsu A, Tanaka SC, Kamagata K, Okada N, Morita K, Kasai K, Abe O. Cross-scanner reproducibility and harmonization of a diffusion MRI structural brain network: A traveling subject study of multi-b acquisition. Neuroimage 2021; 245:118675. [PMID: 34710585 DOI: 10.1016/j.neuroimage.2021.118675] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 09/26/2021] [Accepted: 10/21/2021] [Indexed: 01/18/2023] Open
Abstract
Characterization of brain networks by diffusion MRI (dMRI) has rapidly evolved, and there are ongoing movements toward data sharing and multi-center studies. To extract meaningful information from multi-center data, methods to correct for the bias caused by scanner differences, that is, harmonization, are urgently needed. In this work, we report the cross-scanner differences in structural network analyses using data from nine traveling subjects (four males and five females, 21-49 years-old) who underwent scanning using four 3T scanners (public database available from the Brain/MINDS Beyond Human Brain MRI project (http://mriportal.umin.jp/)). The reliability and reproducibility were compared to those of data from another set of four subjects (all males, 29-42 years-old) who underwent scan-rescan (interval, 105-147 days) with the same scanner as well as scan-rescan data from the Human Connectome Project database. The results demonstrated that the reliability of the edge weights and graph theory metrics was lower for data including different scanners, compared to the scan-rescan with the same scanner. Besides, systematic differences between scanners were observed, indicating the risk of bias in comparing networks obtained from different scanners directly. We further demonstrate that it is feasible to reduce inter-scanner variabilities while preserving the inter-subject differences among healthy individuals by modeling the scanner effects at the level of network matrices, when traveling-subject data are available for calibration between scanners. The present data and results are expected to serve as a basis for developing and evaluating novel harmonization methods.
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Affiliation(s)
- Ryo Kurokawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Kouhei Kamiya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Radiology, Toho University, Tokyo, Japan; Department of Radiology, Juntendo University, Tokyo, Japan.
| | - Shinsuke Koike
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan; University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan; University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan.
| | - Moto Nakaya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Akiko Uematsu
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan.
| | - Saori C Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International (ATR), Kyoto, Japan.
| | - Koji Kamagata
- Department of Radiology, Juntendo University, Tokyo, Japan.
| | - Naohiro Okada
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan; Department of Neuropsychiatry, The University of Tokyo, Tokyo, Japan.
| | - Kentaro Morita
- Department of Neuropsychiatry, The University of Tokyo, Tokyo, Japan.
| | - Kiyoto Kasai
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan; University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), Tokyo, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan; Department of Neuropsychiatry, The University of Tokyo, Tokyo, Japan.
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
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14
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Frau-Pascual A, Augustinack J, Varadarajan D, Yendiki A, Salat DH, Fischl B, Aganj I. Conductance-Based Structural Brain Connectivity in Aging and Dementia. Brain Connect 2021; 11:566-583. [PMID: 34042511 PMCID: PMC8558081 DOI: 10.1089/brain.2020.0903] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Background: Structural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer's disease (AD) progression. Methods: In this work, we used our recently proposed structural connectivity quantification measure derived from diffusion magnetic resonance imaging, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the second phase of Alzheimer's Disease Neuroimaging Initiative and third release in the Open Access Series of Imaging Studies data sets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these data sets to the Human Connectome Project data set, as a reference, and eventually validated externally on two cohorts of the European DTI Study in Dementia database. Results: Our analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among subcortical regions, and cingulate and temporal cortices. Discussion: Results indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anticorrelation in structural connections. Impact statement This work presents a methodology and a set of analyses that open new possibilities in the study of healthy and pathological aging. The methodology used here is sensitive to direct and indirect pathways in deriving brain connectivity measures from diffusion-weighted magnetic resonance imaging, and therefore provides information that many state-of-the-art methods do not account for. As a result, this technique may provide the research community with ways to detect subtle effects of healthy aging and Alzheimer's disease.
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Affiliation(s)
- Aina Frau-Pascual
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Divya Varadarajan
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - David H. Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Iman Aganj
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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15
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Cui D, Hui ES, Cao P. A multi-inversion-recovery magnetic resonance fingerprinting for multi-compartment water mapping. Magn Reson Imaging 2021; 81:82-87. [PMID: 34146651 DOI: 10.1016/j.mri.2021.06.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 06/11/2021] [Accepted: 06/15/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE This study aimed at introducing short-T1/T2 compartment to MR fingerprinting (MRF) at 3 T. Water that is bound to myelin macromolecules have significantly shorter T1 and T2 than free water and can be distinguished from free water by multi-compartment analysis. METHODS We developed a new multi-inversion-recovery (mIR) water mapping-MRF based on an unbalanced steady-state coherent sequence (FISP). mIR pulses with an interval of 400 or 500 repetition times (TRs) were inserted into the conventional FISP MRF sequence. Data from our proposed mIR MRF was used to quantify different compartments, including myelin water, gray matter free water, and white matter free water, of brain water by virtue of the iterative non-negative least square (NNLS) with reweighting. Three healthy volunteers were scanned with mIR MRF on a clinical 3 T MRI. RESULTS Using an extended phase graph simulation, we found that our proposed mIR scheme with four IR pulses allowed differentiation between short and long T1/T2 components. For in vivo experiments, we achieved the quantification of myelin water, gray matter water, and white matter water at an image resolution of 1.17 × 1.17 × 5 mm3/pixel. As compared to the conventional MRF technique with single IR, our proposed mIR improved the detection of myelin water content. In addition, mIR MRF using spiral-in/out trajectory provided a higher signal level compared with that with spiral-out trajectory. Myelin water quantification using mIR MRF with 4 IR and 5 IR pulses were qualitatively similar. Meanwhile, 5 IR MRF showed fewer artifacts in myelin water detection. CONCLUSION We developed a new mIR MRF sequence for the rapid quantification of brain water compartments.
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Affiliation(s)
- Di Cui
- Department of Diagnostic Radiology, The University of Hong Kong, HKSAR, China
| | - Edward S Hui
- Department of Rehabilitation Science, The Hong Kong Polytechnic University, Hong Kong, HKSAR, China
| | - Peng Cao
- Department of Diagnostic Radiology, The University of Hong Kong, HKSAR, China.
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16
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Johnson D, Ricciardi A, Brownlee W, Kanber B, Prados F, Collorone S, Kaden E, Toosy A, Alexander DC, Gandini Wheeler-Kingshott CAM, Ciccarelli O, Grussu F. Comparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis. Front Neurol 2021; 12:662855. [PMID: 34194382 PMCID: PMC8236830 DOI: 10.3389/fneur.2021.662855] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/17/2021] [Indexed: 01/03/2023] Open
Abstract
Background: Neurite orientation dispersion and density imaging (NODDI) and the spherical mean technique (SMT) are diffusion MRI methods providing metrics with sensitivity to similar characteristics of white matter microstructure. There has been limited comparison of changes in NODDI and SMT parameters due to multiple sclerosis (MS) pathology in clinical settings. Purpose: To compare group-wise differences between healthy controls and MS patients in NODDI and SMT metrics, investigating associations with disability and correlations with diffusion tensor imaging (DTI) metrics. Methods: Sixty three relapsing-remitting MS patients were compared to 28 healthy controls. NODDI and SMT metrics corresponding to intracellular volume fraction (vin), orientation dispersion (ODI and ODE), diffusivity (D) (SMT only) and isotropic volume fraction (viso) (NODDI only) were calculated from diffusion MRI data, alongside DTI metrics (fractional anisotropy, FA; axial/mean/radial diffusivity, AD/MD/RD). Correlations between all pairs of MRI metrics were calculated in normal-appearing white matter (NAWM). Associations with expanded disability status scale (EDSS), controlling for age and gender, were evaluated. Patient-control differences were assessed voxel-by-voxel in MNI space controlling for age and gender at the 5% significance level, correcting for multiple comparisons. Spatial overlap of areas showing significant differences were compared using Dice coefficients. Results: NODDI and SMT show significant associations with EDSS (standardised beta coefficient −0.34 in NAWM and −0.37 in lesions for NODDI vin; 0.38 and −0.31 for SMT ODE and vin in lesions; p < 0.05). Significant correlations in NAWM are observed between DTI and NODDI/SMT metrics. NODDI vin and SMT vin strongly correlated (r = 0.72, p < 0.05), likewise NODDI ODI and SMT ODE (r = −0.80, p < 0.05). All DTI, NODDI and SMT metrics detect widespread differences between patients and controls in NAWM (12.57% and 11.90% of MNI brain mask for SMT and NODDI vin, Dice overlap of 0.42). Data Conclusion: SMT and NODDI detect significant differences in white matter microstructure between MS patients and controls, concurring on the direction of these changes, providing consistent descriptors of tissue microstructure that correlate with disability and show alterations beyond focal damage. Our study suggests that NODDI and SMT may play a role in monitoring MS in clinical trials and practice.
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Affiliation(s)
- Daniel Johnson
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Antonio Ricciardi
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Wallace Brownlee
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Baris Kanber
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Ferran Prados
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom.,e-Health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Sara Collorone
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Enrico Kaden
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, United Kingdom.,Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Ahmed Toosy
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre, London, United Kingdom
| | - Daniel C Alexander
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Claudia A M Gandini Wheeler-Kingshott
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Magnetic Resonance Imaging (MRI) 3T Research Centre, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Mondino Foundation, Pavia, Italy
| | - Olga Ciccarelli
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre, London, United Kingdom
| | - Francesco Grussu
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
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17
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Fedeli L, Benelli M, Busoni S, Belli G, Ciccarone A, Coniglio A, Esposito M, Nocetti L, Sghedoni R, Tarducci R, Altabella L, Belligotti E, Bettarini S, Betti M, Caivano R, Carnì M, Chiappiniello A, Cimolai S, Cretti F, Fulcheri C, Gasperi C, Giacometti M, Levrero F, Lizio D, Maieron M, Marzi S, Mascaro L, Mazzocchi S, Meliadò G, Morzenti S, Niespolo A, Noferini L, Oberhofer N, Orsingher L, Quattrocchi M, Ricci A, Savini A, Taddeucci A, Testa C, Tortoli P, Gobbi G, Gori C, Bernardi L, Giannelli M, Mazzoni LN. On the dependence of quantitative diffusion-weighted imaging on scanner system characteristics and acquisition parameters: A large multicenter and multiparametric phantom study with unsupervised clustering analysis. Phys Med 2021; 85:98-106. [PMID: 33991807 DOI: 10.1016/j.ejmp.2021.04.020] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/31/2021] [Accepted: 04/23/2021] [Indexed: 11/25/2022] Open
Abstract
PURPOSE The purpose of this multicenter phantom study was to exploit an innovative approach, based on an extensive acquisition protocol and unsupervised clustering analysis, in order to assess any potential bias in apparent diffusion coefficient (ADC) estimation due to different scanner characteristics. Moreover, we aimed at assessing, for the first time, any effect of acquisition plan/phase encoding direction on ADC estimation. METHODS Water phantom acquisitions were carried out on 39 scanners. DWI acquisitions (b-value = 0-200-400-600-800-1000 s/mm2) with different acquisition plans (axial, coronal, sagittal) and phase encoding directions (anterior/posterior and right/left, for the axial acquisition plan), for 3 orthogonal diffusion weighting gradient directions, were performed. For each acquisition setup, ADC values were measured in-center and off-center (6 different positions), resulting in an entire dataset of 84 × 39 = 3276 ADC values. Spatial uniformity of ADC maps was assessed by means of the percentage difference between off-center and in-center ADC values (Δ). RESULTS No significant dependence of in-center ADC values on acquisition plan/phase encoding direction was found. Ward unsupervised clustering analysis showed 3 distinct clusters of scanners and an association between Δ-values and manufacturer/model, whereas no association between Δ-values and maximum gradient strength, slew rate or static magnetic field strength was revealed. Several acquisition setups showed significant differences among groups, indicating the introduction of different biases in ADC estimation. CONCLUSIONS Unsupervised clustering analysis of DWI data, obtained from several scanners using an extensive acquisition protocol, allows to reveal an association between measured ADC values and manufacturer/model of scanner, as well as to identify suboptimal DWI acquisition setups for accurate ADC estimation.
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Affiliation(s)
- Luca Fedeli
- S.O.C. Fisica Sanitaria Pistoia-Prato, A.U.S.L. Toscana Centro, Italy
| | - Matteo Benelli
- Bioinformatics Unit, Hospital of Prato, A.U.S.L. Toscana Centro, Italy
| | - Simone Busoni
- U.O.C. Fisica Sanitaria, A.O.U. Careggi, Firenze, Italy
| | - Giacomo Belli
- U.O.C. Fisica Sanitaria, A.O.U. Careggi, Firenze, Italy
| | | | - Angela Coniglio
- Department of Medical Physics, P.O. S. Filippo Neri, Roma, Italy
| | - Marco Esposito
- S.C. Fisica Sanitaria Firenze-Empoli, A.U.S.L. Toscana Centro, Firenze, Italy
| | - Luca Nocetti
- Servizio di Fisica Medica, A.O.U. Policlinico di Modena, Modena, Italy
| | - Roberto Sghedoni
- Fisica Medica, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | | | - Luisa Altabella
- Medical Physics Department, Hospital of Trento, APSS, Trento, Italy
| | - Eleonora Belligotti
- Fisica Medica ed Alte Tecnologie, A.O. Ospedali Riuniti Marche Nord, Pesaro, Italy
| | - Silvia Bettarini
- U.O.C. Fisica Sanitaria, A.O.U. Careggi, Firenze, Italy; Università degli Studi di Firenze, Firenze, Italy
| | - Margherita Betti
- S.O.C. Fisica Sanitaria Pistoia-Prato, A.U.S.L. Toscana Centro, Italy
| | - Rocchina Caivano
- U.O. Radioterapia Oncologica e Fisica Sanitaria, I.R.C.C.S. CROB, Rionero in Vulture (PZ), Italy
| | - Marco Carnì
- U.O.D. Fisica Sanitaria, A.O.U. Policlinico Umberto I, Roma, Italy
| | | | - Sara Cimolai
- U.O. Fisica Sanitaria, U.L.S.S. 2 Marca Trevigiana, Treviso, Italy
| | - Fabiola Cretti
- U.S.C. Fisica Sanitaria, A.O. Papa Giovanni XXIII, Bergamo, Italy
| | | | - Chiara Gasperi
- U.O.S.D. Fisica Sanitaria Arezzo, A.U.S.L. Toscana Sud Est, Arezzo, Italy
| | - Mara Giacometti
- S.O.D. Fisica Sanitaria, A.O.U. Ospedali Riuniti di Ancona, Ancona, Italy
| | - Fabrizio Levrero
- U.O. Fisica Sanitaria, Ospedale Policlinico San Martino, Genova, Italy
| | - Domenico Lizio
- Fisica Sanitaria, A.S.S.T. Grande Ospedale Metropolitano Niguarda, Milano, Italy
| | - Marta Maieron
- S.O.C. Fisica Sanitaria, A.S.U.I. Udine S. Maria della Misericordia, Udine, Italy
| | - Simona Marzi
- S.C. Laboratorio di Fisica Medica e Sistemi Esperti, Istituto Nazionale Tumori Regina Elena, Roma, Italy
| | - Lorella Mascaro
- U.O.C. Fisica Sanitaria, A.S.S.T. Spedali Civili, Brescia, Italy
| | - Silvia Mazzocchi
- S.C. Fisica Sanitaria Firenze-Empoli, A.U.S.L. Toscana Centro, Firenze, Italy
| | - Gabriele Meliadò
- U.O.C. Fisica Sanitaria, A.O.U. Integrata di Verona, Verona, Italy
| | | | - Alessandra Niespolo
- U.O.C. Fisica Sanitaria Area Nord, A.U.S.L. Toscana Nord Ovest, Lucca, Italy
| | | | - Nadia Oberhofer
- Servizio Aziendale di Fisica Sanitaria, A.S. dell'Alto Adige, Bolzano, Italy
| | - Laura Orsingher
- U.O. Fisica Sanitaria, U.L.S.S. 2 Marca Trevigiana, Treviso, Italy
| | | | | | - Alessandro Savini
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
| | | | - Claudia Testa
- Dipartimento di Fisica e Astronomia, Università di Bologna, Bologna, Italy
| | - Paolo Tortoli
- U.O.C. Fisica Sanitaria, A.O.U. Careggi, Firenze, Italy; Università degli Studi di Firenze, Firenze, Italy
| | - Gianni Gobbi
- Università degli Studi di Perugia, Perugia, Italy
| | - Cesare Gori
- Università degli Studi di Firenze, Firenze, Italy
| | - Luca Bernardi
- S.O.C. Fisica Sanitaria Pistoia-Prato, A.U.S.L. Toscana Centro, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy.
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18
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Wang F, Dong Z, Tian Q, Liao C, Fan Q, Hoge WS, Keil B, Polimeni JR, Wald LL, Huang SY, Setsompop K. In vivo human whole-brain Connectom diffusion MRI dataset at 760 µm isotropic resolution. Sci Data 2021; 8:122. [PMID: 33927203 PMCID: PMC8084962 DOI: 10.1038/s41597-021-00904-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 03/26/2021] [Indexed: 01/18/2023] Open
Abstract
We present a whole-brain in vivo diffusion MRI (dMRI) dataset acquired at 760 μm isotropic resolution and sampled at 1260 q-space points across 9 two-hour sessions on a single healthy participant. The creation of this benchmark dataset is possible through the synergistic use of advanced acquisition hardware and software including the high-gradient-strength Connectom scanner, a custom-built 64-channel phased-array coil, a personalized motion-robust head stabilizer, a recently developed SNR-efficient dMRI acquisition method, and parallel imaging reconstruction with advanced ghost reduction algorithm. With its unprecedented resolution, SNR and image quality, we envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance the understanding of human brain structures and connectivity. This comprehensive dataset can also be used as a test bed for new modeling, sub-sampling strategies, denoising and processing algorithms, potentially providing a common testing platform for further development of in vivo high resolution dMRI techniques. Whole brain anatomical T1-weighted and T2-weighted images at submillimeter scale along with field maps are also made available.
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Affiliation(s)
- Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA.
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA.
| | - Zijing Dong
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Congyu Liao
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
| | - W Scott Hoge
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Boris Keil
- Department of Life Science Engineering, Institute of Medical Physics and Radiation Protection, Giessen, Germany
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
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19
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Abstract
Since many years, magnetic resonance imaging (MRI) and positron emission tomography (PET) have a prominent role in neurodegenerative disorders and dementia, not only in a research setting but also in a clinical setting. For several decades, information from both modalities is combined ranging from individual visual assessments to fully integrating all images. Several tools are available to coregister images from MRI and PET and to covisualize these images. When studying neurodegenerative disorders with PET it is important to perform a partial volume correction and this can be done using the structural information obtained by MRI. With the advent of PET/MR, the question arises in how far this hybrid imaging modality is an added value compared to combining PET and MRI data from two separate modalities. One issue in PET/MR is still not yet completely settled, that is, the attenuation correction. This is of less importance for visual assessments but it can become an issue when combining data from PET/CT and PET/MR scanners in multicenter studies or when using cut-off values to classify patients. Simultaneous imaging has clearly some advantages: for the patient it is beneficial to have only one scan session instead of two but also in cases in which PET data are related to functional of physiological data acquired with MRI (such as functional MRI or arterial spin labeling). However, the most important benefit is currently the more integrated use of PET and MRI. This is also possible with separate measurements but requires more streamlining of the whole process. In that case coregistration of images is mandatory. It needs to be determined in which cases simultaneous PET/MRI leads to new insights or improved diagnosis compared to multimodal imaging using dedicated scanners.
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Affiliation(s)
- Patrick Dupont
- KU Leuven, Leuven Brain Institute, Department of Neurosciences, Laboratory for Cognitive Neurology, Leuven, Belgium; University of Stellenbosch, Department of Nuclear Medicine, Cape Town, South Africa.
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20
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Kamiya K, Kamagata K, Ogaki K, Hatano T, Ogawa T, Takeshige-Amano H, Murata S, Andica C, Murata K, Feiweier T, Hori M, Hattori N, Aoki S. Brain White-Matter Degeneration Due to Aging and Parkinson Disease as Revealed by Double Diffusion Encoding. Front Neurosci 2020; 14:584510. [PMID: 33177985 PMCID: PMC7594529 DOI: 10.3389/fnins.2020.584510] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 09/22/2020] [Indexed: 11/16/2022] Open
Abstract
Microstructure imaging by means of multidimensional diffusion encoding is increasingly applied in clinical research, with expectations that it yields a parameter that better correlates with clinical disability than current methods based on single diffusion encoding. Under the assumption that diffusion within a voxel can be well described by a collection of diffusion tensors, several parameters of this diffusion tensor distribution can be derived, including mean size, variance of sizes, orientational dispersion, and microscopic anisotropy. The information provided by multidimensional diffusion encoding also enables us to decompose the sources of the conventional fractional anisotropy and mean kurtosis. In this study, we explored the utility of the diffusion tensor distribution approach for characterizing white-matter degeneration in aging and in Parkinson disease by using double diffusion encoding. Data from 23 healthy older subjects and 27 patients with Parkinson disease were analyzed. Advanced age was associated with greater mean size and size variances, as well as smaller microscopic anisotropy. By analyzing the parameters underlying diffusion kurtosis, we found that the reductions of kurtosis in aging and Parkinson disease reported in the literature are likely driven by the reduction in microscopic anisotropy. Furthermore, microscopic anisotropy correlated with the severity of motor impairment in the patients with Parkinson disease. The present results support the use of multidimensional diffusion encoding in clinical studies and are encouraging for its future clinical implementation.
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Affiliation(s)
- Kouhei Kamiya
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.,Department of Radiology, Toho University, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Kotaro Ogaki
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Taku Hatano
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Takashi Ogawa
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | | | - Syo Murata
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Christina Andica
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | | | | | - Masaaki Hori
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan.,Department of Radiology, Toho University, Tokyo, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
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21
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Laib Z, Ahmed Sid F, Abed-Meraim K, Ouldali A. Estimation error bound for GRAPPA diffusion-weighted MRI. Magn Reson Imaging 2020; 74:181-194. [PMID: 33010376 DOI: 10.1016/j.mri.2020.09.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 08/26/2020] [Accepted: 09/23/2020] [Indexed: 01/08/2023]
Abstract
In recent years, diffusion weight magnetic resonance imaging (DW-MRI) has become one of the most important MRI imaging modalities. The importance of the DW-MRI grew thanks to the combination of parallel magnetic resonance imaging (pMRI) techniques with the echo-planar imaging (EPI), which minimize scan time and lead to reduced distortion, allowing the DW-MRI to become a routine clinical exam. Additionally, this has brought various new parameters that influence image quality and biomarkers used in DW-MRI. This work aims to investigate the effects of these parameters on the estimation quality, by using the Cramér-Rao bound tool, which gives analytical expressions of the lower limit on the estimation error variance of different DW-MRI variables when using the pMRI technique. In particular, these bounds will be used to study and optimize the impact of different factors of generalized autocalibrating partially parallel acquisition (GRAPPA) technique and system parameters on the estimation quality of the desired clinical metrics. Moreover, the obtained results of this study can be exploited and adapted in all human body DW-MRI clinical routines, further improving disease diagnosis, and tractography studies.
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Affiliation(s)
- Zohir Laib
- Laboratoire traitement du signal, EMP, BP 17 Bordj El Bahri, 16111 Algiers, Algeria.
| | - Farid Ahmed Sid
- ParIMéd/LRPE,FEI,USTHB, BP 32 El Alia, Bab Ezzouar, 16111 Algiers, Algeria
| | - Karim Abed-Meraim
- PRISME Laboratory, University of Orléans, 12 Rue de Blois, 45067 Orléans, France
| | - Aziz Ouldali
- Laboratoire signaux et systemes, University of Mostaganem, BP 002 Kharouba, 27000 Mostaganem, Algeria
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22
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Meira AT, Arruda WO, Ono SE, Franklin GL, de Carvalho Neto A, Raskin S, Ashizawa T, Camargo CHF, Teive HA. Analysis of diffusion tensor parameters in spinocerebellar ataxia type 3 and type 10 patients. Parkinsonism Relat Disord 2020; 78:73-78. [DOI: 10.1016/j.parkreldis.2020.06.460] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 06/29/2020] [Accepted: 06/30/2020] [Indexed: 02/08/2023]
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23
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Müller HP, Roselli F, Rasche V, Kassubek J. Diffusion Tensor Imaging-Based Studies at the Group-Level Applied to Animal Models of Neurodegenerative Diseases. Front Neurosci 2020; 14:734. [PMID: 32982659 PMCID: PMC7487414 DOI: 10.3389/fnins.2020.00734] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 06/22/2020] [Indexed: 12/11/2022] Open
Abstract
The understanding of human and non-human microstructural brain alterations in the course of neurodegenerative diseases has substantially improved by the non-invasive magnetic resonance imaging (MRI) technique of diffusion tensor imaging (DTI). Animal models (including disease or knockout models) allow for a variety of experimental manipulations, which are not applicable to humans. Thus, the DTI approach provides a promising tool for cross-species cross-sectional and longitudinal investigations of the neurobiological targets and mechanisms of neurodegeneration. This overview with a systematic review focuses on the principles of DTI analysis as used in studies at the group level in living preclinical models of neurodegeneration. The translational aspect from in-vivo animal models toward (clinical) applications in humans is covered as well as the DTI-based research of the non-human brains' microstructure, the methodological aspects in data processing and analysis, and data interpretation at different abstraction levels. The aim of integrating DTI in multiparametric or multimodal imaging protocols will allow the interrogation of DTI data in terms of directional flow of information and may identify the microstructural underpinnings of neurodegeneration-related patterns.
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Affiliation(s)
| | - Francesco Roselli
- Department of Neurology, University of Ulm, Ulm, Germany.,German Center for Neurodegenerative Diseases (DZNE), Ulm, Germany
| | - Volker Rasche
- Core Facility Small Animal MRI, University of Ulm, Ulm, Germany
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany
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24
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Kamiya K, Hori M, Aoki S. NODDI in clinical research. J Neurosci Methods 2020; 346:108908. [PMID: 32814118 DOI: 10.1016/j.jneumeth.2020.108908] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 08/08/2020] [Accepted: 08/09/2020] [Indexed: 12/11/2022]
Abstract
Diffusion MRI (dMRI) has proven to be a useful imaging approach for both clinical diagnosis and research investigating the microstructures of nervous tissues, and it has helped us to better understand the neurophysiological mechanisms of many diseases. Though diffusion tensor imaging (DTI) has long been the default tool to analyze dMRI data in clinical research, acquisition with stronger diffusion weightings beyond the DTI regimen is now possible with modern clinical scanners, potentially enabling even more detailed characterization of tissue microstructures. To take advantage of such data, neurite orientation dispersion and density imaging (NODDI) has been proposed as a way to relate the dMRI signal to tissue features via biophysically inspired modeling. The number of reports demonstrating the potential clinical utility of NODDI is rapidly increasing. At the same time, the pitfalls and limitations of NODDI, and general challenges in microstructure modeling, are becoming increasingly recognized by clinicians. dMRI microstructure modeling is a rapidly evolving field with great promise, where people from different scientific backgrounds, such as physics, medicine, biology, neuroscience, and statistics, are collaborating to build novel tools that contribute to improving human healthcare. Here, we review the applications of NODDI in clinical research and discuss future perspectives for investigations toward the implementation of dMRI microstructure imaging in clinical practice.
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Affiliation(s)
- Kouhei Kamiya
- Department of Radiology, The University of Tokyo, Tokyo, Japan; Department of Radiology, Juntendo University, Tokyo, Japan; Department of Radiology, Toho University, Tokyo, Japan.
| | - Masaaki Hori
- Department of Radiology, Juntendo University, Tokyo, Japan; Department of Radiology, Toho University, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University, Tokyo, Japan
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25
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Correia MM, Rittman T, Barnes CL, Coyle-Gilchrist IT, Ghosh B, Hughes LE, Rowe JB. Towards accurate and unbiased imaging-based differentiation of Parkinson's disease, progressive supranuclear palsy and corticobasal syndrome. Brain Commun 2020; 2:fcaa051. [PMID: 32671340 PMCID: PMC7325838 DOI: 10.1093/braincomms/fcaa051] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 01/17/2020] [Accepted: 02/12/2020] [Indexed: 12/20/2022] Open
Abstract
The early and accurate differential diagnosis of parkinsonian disorders is still a significant challenge for clinicians. In recent years, a number of studies have used magnetic resonance imaging data combined with machine learning and statistical classifiers to successfully differentiate between different forms of Parkinsonism. However, several questions and methodological issues remain, to minimize bias and artefact-driven classification. In this study, we compared different approaches for feature selection, as well as different magnetic resonance imaging modalities, with well-matched patient groups and tightly controlling for data quality issues related to patient motion. Our sample was drawn from a cohort of 69 healthy controls, and patients with idiopathic Parkinson’s disease (n = 35), progressive supranuclear palsy Richardson’s syndrome (n = 52) and corticobasal syndrome (n = 36). Participants underwent standardized T1-weighted and diffusion-weighted magnetic resonance imaging. Strict data quality control and group matching reduced the control and patient numbers to 43, 32, 33 and 26, respectively. We compared two different methods for feature selection and dimensionality reduction: whole-brain principal components analysis, and an anatomical region-of-interest based approach. In both cases, support vector machines were used to construct a statistical model for pairwise classification of healthy controls and patients. The accuracy of each model was estimated using a leave-two-out cross-validation approach, as well as an independent validation using a different set of subjects. Our cross-validation results suggest that using principal components analysis for feature extraction provides higher classification accuracies when compared to a region-of-interest based approach. However, the differences between the two feature extraction methods were significantly reduced when an independent sample was used for validation, suggesting that the principal components analysis approach may be more vulnerable to overfitting with cross-validation. Both T1-weighted and diffusion magnetic resonance imaging data could be used to successfully differentiate between subject groups, with neither modality outperforming the other across all pairwise comparisons in the cross-validation analysis. However, features obtained from diffusion magnetic resonance imaging data resulted in significantly higher classification accuracies when an independent validation cohort was used. Overall, our results support the use of statistical classification approaches for differential diagnosis of parkinsonian disorders. However, classification accuracy can be affected by group size, age, sex and movement artefacts. With appropriate controls and out-of-sample cross validation, diagnostic biomarker evaluation including magnetic resonance imaging based classifiers may be an important adjunct to clinical evaluation.
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Affiliation(s)
- Marta M Correia
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation Trust, University of Cambridge, Cambridge, UK
| | | | - Ian T Coyle-Gilchrist
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation Trust, University of Cambridge, Cambridge, UK
| | - Boyd Ghosh
- Wessex Neurological Centre, University Hospital Southampton NHS Foundation Trust, UK
| | - Laura E Hughes
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK.,Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation Trust, University of Cambridge, Cambridge, UK
| | - James B Rowe
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK.,Department of Clinical Neurosciences and Cambridge University Hospitals NHS Foundation Trust, University of Cambridge, Cambridge, UK.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark
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26
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Luque Laguna PA, Combes AJE, Streffer J, Einstein S, Timmers M, Williams SCR, Dell'Acqua F. Reproducibility, reliability and variability of FA and MD in the older healthy population: A test-retest multiparametric analysis. NEUROIMAGE-CLINICAL 2020; 26:102168. [PMID: 32035272 PMCID: PMC7011084 DOI: 10.1016/j.nicl.2020.102168] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2019] [Revised: 01/10/2020] [Accepted: 01/10/2020] [Indexed: 12/13/2022]
Abstract
In older healthy subjects, FA and MD show overall good test-retest reliability & reproducibility. MD is sistematically more reproducible than FA across the entire brain anatomy. FA is more reliable than MD in subcortical white matter regions. In high reliability & low reproducibility regions, variability between subjects is high and statistical power is low. In low reliability & high reproducibility regions, variability between subjects is low and statistical power is high.
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Affiliation(s)
- Pedro A Luque Laguna
- Department 5 of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; Natbrainlab, Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK; Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK.
| | - Anna J E Combes
- Department 5 of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Johannes Streffer
- UCB Biopharma SPRL, Chemin du Foriest B-1420 Braine-l'Alleud, Belgium; Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Steven Einstein
- Janssen Research and Development LLC, Titusville, NJ, US; UCB Biopharma SPRL, Chemin du Foriest B-1420 Braine-l'Alleud, Belgium
| | - Maarten Timmers
- Janssen Research and Development, a division of Janssen Pharmaceutica NV, Beerse, Belgium; Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Steve C R Williams
- Department 5 of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - Flavio Dell'Acqua
- Natbrainlab, Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK; Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK.
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27
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Indovina I, Conti A, Lacquaniti F, Staab JP, Passamonti L, Toschi N. Reduced betweenness centrality of a sensory-motor vestibular network in subclinical agoraphobia. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:4342-4345. [PMID: 31946829 DOI: 10.1109/embc.2019.8857332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Agoraphobic patients feel dizzy in crowded open spaces and respond to this symptom with excessive fear and avoidance. These clinical features show great similitude with the newly defined syndrome of persistent postural perceptual dizziness (PPPD). Patients with PPPD show decreased activity and connectivity in regions of the vestibular cortex. Due to the great overlap between these two conditions, we hypothesized that individuals with sub-clinical agoraphobia would show reduction in the connectivity features of these regions. We selected a group of healthy individuals from the Human Connectome Project that self-reported agoraphobia episodes, and compared it with a control group. We accurately matched the two groups for psychological measures and personality traits in order to study the neural correlates of vestibular symptoms independently of possible psychiatric vulnerabilities. We found that the agoraphobia group showed reduced betweenness centrality of a network encompassing key regions of the vestibular cortex. Dysfunctions of the vestibular cortex may explain the dizziness symptom for a disorder previously labelled as psychogenic.
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28
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Andica C, Kamagata K, Hatano T, Saito Y, Ogaki K, Hattori N, Aoki S. MR Biomarkers of Degenerative Brain Disorders Derived From Diffusion Imaging. J Magn Reson Imaging 2019; 52:1620-1636. [PMID: 31837086 PMCID: PMC7754336 DOI: 10.1002/jmri.27019] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 11/24/2019] [Accepted: 11/26/2019] [Indexed: 12/12/2022] Open
Abstract
The incidence of neurodegenerative diseases has shown an increasing trend. These conditions typically cause progressive functional disability. Identification of robust biomarkers of neurodegenerative diseases is a key imperative to facilitate early identification of the pathological features and to foster a better understanding of the pathogenetic mechanisms of individual diseases. Diffusion tensor imaging (DTI) is the most widely used diffusion MRI technique for assessment of neurodegenerative diseases. The DTI parameters are promising biomarkers for evaluation of microstructural changes; however, some limitations of DTI restrict its wider clinical use. New diffusion MRI techniques, such as diffusion kurtosis imaging (DKI), bi-tensor DTI, and neurite orientation density and dispersion imaging (NODDI) have been demonstrated to provide value addition to DTI for evaluation of neurodegenerative diseases. In this review article, we summarize the key technical aspects and provide an overview of the current state of knowledge regarding the role of DKI, bi-tensor DTI, and NODDI as biomarkers of microstructural changes in representative neurodegenerative diseases including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1620-1636.
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Affiliation(s)
- Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Taku Hatano
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Radiological Sciences, Tokyo Metropolitan University, Graduate School of Human Health Sciences, Tokyo, Japan
| | - Kotaro Ogaki
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
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29
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Toschi N, Gisbert RA, Passamonti L, Canals S, De Santis S. Multishell diffusion imaging reveals sex-specific trajectories of early white matter degeneration in normal aging. Neurobiol Aging 2019; 86:191-200. [PMID: 31902522 DOI: 10.1016/j.neurobiolaging.2019.11.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 10/08/2019] [Accepted: 11/21/2019] [Indexed: 02/08/2023]
Abstract
During aging, human white matter (WM) is subject to dynamic structural changes which have a deep impact on healthy and pathological evolution of the brain through the lifespan; characterizing this pattern is of key importance for understanding brain development, maturation, and aging as well as for studying its pathological alterations. Diffusion magnetic resonance imaging (MRI) can provide a quantitative assessment of the white-matter microstructural organization that characterizes these trajectories. Here, we use both conventional and advanced diffusion MRI in a cohort of 91 individuals (age range: 13-62 years) to study region- and sex-specific features of WM microstructural integrity in healthy aging. We focus on the age at which microstructural imaging parameters invert their development trend as the time point which marks the onset of microstructural decline in WM. Importantly, our results indicate that age-related brain changes begin earlier in males than females and affect more frontal regions-in accordance with evolutionary theories and numerous evidences across non-MRI domains. Advanced diffusion MRI reveals age-related WM modification patterns which cannot be detected using conventional diffusion tensor imaging.
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Affiliation(s)
- Nicola Toschi
- Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA; Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | | | - Luca Passamonti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Istituto di Bioimmagini e Fisiologia Molecolare (IBFM), Consiglio Nazionale delle Ricerche (CNR), Segrate, Milano, Italia
| | - Santiago Canals
- Instituto de Neurociencias de Alicante (CSIC-UMH), San Juan de Alicante, Spain
| | - Silvia De Santis
- Instituto de Neurociencias de Alicante (CSIC-UMH), San Juan de Alicante, Spain; Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK.
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30
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Stieb S, Klarhoefer M, Finkenstaedt T, Wurnig MC, Becker AS, Ciritsis A, Rossi C. Correction for fast pseudo-diffusive fluid motion contaminations in diffusion tensor imaging. Magn Reson Imaging 2019; 66:50-56. [PMID: 31655141 DOI: 10.1016/j.mri.2019.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 07/18/2019] [Accepted: 09/15/2019] [Indexed: 11/26/2022]
Abstract
In this prospective study, we quantified the fast pseudo-diffusion contamination by blood perfusion or cerebrospinal fluid (CSF) intravoxel incoherent movements on the measurement of the diffusion tensor metrics in healthy brain tissue. Diffusion-weighted imaging (TR/TE = 4100 ms/90 ms; b-values: 0, 5, 10, 20, 35, 55, 80, 110, 150, 200, 300, 500, 750, 1000, 1300 s/mm2, 20 diffusion-encoding directions) was performed on a cohort of five healthy volunteers at 3 Tesla. The projections of the diffusion tensor along each diffusion-encoding direction were computed using a two b-value approach (2b), by fitting the signal to a monoexponential curve (mono), and by correcting for fast pseudo-diffusion compartments using the biexponential intravoxel incoherent motion model (IVIM) (bi). Fractional anisotropy (FA) and mean diffusivity (MD) of the diffusion tensor were quantified in regions of interest drawn over white matter areas, gray matter areas, and the ventricles. A significant dependence of the MD from the evaluation method was found in all selected regions. A lower MD was computed when accounting for the fast-diffusion compartments. A larger dependence was found in the nucleus caudatus (bi: median 0.86 10-3 mm2/s, Δ2b: -11.2%, Δmono: -14.4%; p = 0.007), in the anterior horn (bi: median 2.04 10-3 mm2/s, Δ2b: -9.4%, Δmono: -11.5%, p = 0.007) and in the posterior horn of the lateral ventricles (bi: median 2.47 10-3 mm2/s, Δ2b: -5.5%, Δmono: -11.7%; p = 0.007). Also for the FA, the signal modeling affected the computation of the anisotropy metrics. The deviation depended on the evaluated region with significant differences mainly in the nucleus caudatus (bi: median 0.15, Δ2b: +39.3%, Δmono: +14.7%; p = 0.022) and putamen (bi: median 0.19, Δ2b: +3.1%, Δmono: +17.3%; p = 0.015). Fast pseudo-diffusive regimes locally affect diffusion tensor imaging (DTI) metrics in the brain. Here, we propose the use of an IVIM-based method for correction of signal contaminations through CSF or perfusion.
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Affiliation(s)
- Sonja Stieb
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland.
| | | | - Tim Finkenstaedt
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
| | - Moritz C Wurnig
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
| | - Anton S Becker
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
| | - Alexander Ciritsis
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
| | - Cristina Rossi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
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Handedness Side and Magnetization Transfer Ratio in the Primary Sensorimotor Cortex Central Sulcus. BIOMED RESEARCH INTERNATIONAL 2019; 2019:5610849. [PMID: 31467897 PMCID: PMC6699472 DOI: 10.1155/2019/5610849] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 06/24/2019] [Accepted: 07/21/2019] [Indexed: 11/17/2022]
Abstract
Left-handers show lower asymmetry in manual ability when compared to right-handers. Unlike right-handers, left-handers do not show larger deactivation of the ipsilateral primary sensorimotor (SM1) cortex on functional magnetic resonance imaging when moving their dominant than their nondominant hand. However, it should be noted that morphometric MRI studies have reported no differences between right-handers and left-handers in volume, thickness, or surface area of the SM1 cortex. In this regard, magnetization transfer (MT) imaging is a technique with the potential to provide information on microstructural organization and macromolecular content of tissue. In particular, MT ratio index of the brain gray matter is assumed to reflect the variable content of afferent or efferent myelinated fibers, with higher MT ratio values being associated with increased fibers number or degree of myelination. The aim of this study was hence to assess, for the first time, through quantitative MT ratio measurements, potential differences in microstructural organization/characteristics of SM1 cortex between left- and right-handers, which could underlay handedness side. Nine left-handed and 9 right-handed healthy subjects, as determined by the Edinburgh handedness inventory, were examined with T1-weighted and MT-weighted imaging on a 3 T scanner. The hands of subjects were kept still during all acquisitions. Using FreeSurfer suite and the automatic anatomical labeling parcellations defined by the Destrieux atlas, we measured MT ratio, as well as cortical thickness, in three regions of interests corresponding to the precentral gyrus, the central sulcus, and the postcentral gyrus in the bilateral SM1 cortex. No significant difference between left- and right-handers was revealed in the thickness of the three partitions of the SM1 cortex. However, left-handers showed a significantly (p = 0.007) lower MT ratio (31.92% ± 0.96%) in the right SM1 central sulcus (i.e., the hand representation area for left-handers) as compared to right-handers (33.28% ± 0.94%). The results of this preliminary study indicate that quantitative MT imaging, unlike conventional morphometric MRI measurements, can be a useful tool to reveal, in SM1 cortex, potential microstructural correlates of handedness side.
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Sporn L, MacMillan EL, Ge R, Greenway K, Vila-Rodriguez F, Laule C. Longer Repetition Time Proton MR Spectroscopy Shows Increasing Hippocampal and Parahippocampal Metabolite Concentrations with Aging. J Neuroimaging 2019; 29:592-597. [PMID: 31273871 DOI: 10.1111/jon.12648] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 06/11/2019] [Accepted: 06/14/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND AND PURPOSE Previous magnetic resonance spectroscopy (MRS) studies have concluded that hippocampal and parahippocampal metabolite concentrations remain stable during healthy adult aging. However, these studies used short repetition times (TR ≤ 2 seconds), which lead to incomplete longitudinal magnetization recovery, and thus, heavily T1 -weighted measurements. It is important to accurately characterize brain metabolites changes with age to enable appropriate interpretations of MRS findings in the context of neurodegenerative diseases. Our goal was to assess hippocampal brain metabolite concentrations in a large cohort of diversely aged healthy volunteers using a longer TR of 4 seconds. METHODS Left hippocampal MR spectra were collected from 38 healthy volunteers at 3T. Absolute metabolite concentrations were determined for total N-acetyl-aspartate (tNAA), total creatine (tCr), total choline (tCho), glutamate and glutamine (Glx), and myoinositol (mI). Individual partial correlations between each metabolite with age were assessed using demographic information and voxel compartmentation as confounders. RESULTS Hippocampal tNAA, tCr, tCho, and mI all increased with age (NAA: R2 = .17, P = .041; tCr: R2 = .45, P = .0002; tCho: R2 = .37, P = .001; mI: R2 = .44, P = .0003). There were no relationships between age and signal to noise ratio, linewidth, or scan date, indicating the correlations were not confounded by spectral quality. Furthermore, we did not observe a trend with age in the voxel tissue compartmentations. CONCLUSIONS We observed increases in hippocampal/parahippocampal metabolite concentrations with age, a finding that is in contrast to previous literature. Our findings illustrate the importance of using a sufficiently long TR in MRS to avoid T1 -relaxation effects influencing the measurement of absolute metabolite concentrations.
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Affiliation(s)
- Leo Sporn
- Department of Physics & Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada
| | - Erin L MacMillan
- School of Mechatronic Systems Engineering, Faculty of Applied Sciences, Simon Fraser University, Vancouver, British Columbia, Canada.,Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada.,Philips, Markham, Ontario, Canada
| | - Ruiyang Ge
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Kyle Greenway
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Fidel Vila-Rodriguez
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Cornelia Laule
- Department of Physics & Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada.,Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada.,Pathology & Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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Minosse S, Floris R, Nucci C, Toschi N, Garaci F, Martucci A, Lanzafame S, Di Giuliano F, Picchi E, Cesareo M, Mancino R, Guerrisi M. Disruption of brain network organization in primary open angle glaucoma. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:4338-4341. [PMID: 31946828 DOI: 10.1109/embc.2019.8857290] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) is commonly employed to study changes in functional brain connectivity. Recently, the hypothesis of a brain involvement in primary open angle glaucoma has sprung interest for neuroimaging studies in this pathology. The purpose of this study is to evaluate a putative reorganization of brain networks in glaucomatous patients through graph-theoretical measures of integration, segregation and centrality by exploiting a multivariate networks association measure and a recently introduced global and local brain network disruption index. Nineteen glaucoma patients and sixteen healthy control subjects (age: 50 - 76, mean 61 years) underwent rs-fMRI examination at 3T. After preprocessing, rs-fMRI time series were parcellated into 116 regions (AAL atlas), adjacency matrices were computed based on partial correlations and graph-theoretical measures of integration, segregation and centrality as well as group-wise and subject-wise disruption index estimates were generated for all subjects. We found that the group-wise disruption index was negative and statistically different from 0 in for all graph theoretical metrics. Additionally, statistically significant group-wise differences in subject-wise disruption indexes were found in all local metrics. The differences in local network measures highlight cerebral reorganization of brain networks in glaucoma patients, supporting the interpretation of glaucoma as central nervous system disease, likely part of the heterogeneous group of recently described disconnection syndromes.
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34
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Duggento A, Guerrisi M, Toschi N. Recurrent neural networks for reconstructing complex directed brain connectivity. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:6418-6421. [PMID: 31947311 DOI: 10.1109/embc.2019.8856721] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
While Granger Causality(GC)-based approaches have been widely employed in a vast number of problems in network science, the vast majority of GC applications are based on linear multivariate autoregressive (MVAR) models. However, it is well known that real-life system (and biological networks in particular) exhibit notable nonlinear behavior, hence undermining that validity of MVAR-based approaches to estimating GC (MVAR-GC). In this paper, we define a novel approach to estimating nonlinear, directed within-network interactions based on a specific class of recurrent neural networks (RNN) termed echo-state networks (ESN). We reformulate the classical GC framework in terms of ESN-based models for multivariate signals generated by arbitrarily complex networks, and characterize the ability of our ESN-based Granger Causality (ES-GC) to capture nonlinear causal relations by simulating multivariate coupling in a network of nonlinearly interacting, noisy Duffing oscillators operating in a chaotic regime. Synthetic validation shows a net advantage of ES-GC over all other estimators in detecting nonlinear, causal links. We then explore the structure of EC-GC networks in the human brain in functional MRI data from 1003 healthy subjects scanned at rest at 3T, discovering previously unknown between-network interactions. In summary, ES-GC performs significantly better than commonly used and recently developed GC detection tools, making it a superior tool for the analysis of e.g. multivariate biological networks.
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35
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Rao JS, Liu Z, Zhao C, Wei RH, Liu RX, Zhao W, Zhou X, Tian PY, Yang ZY, Li XG. Image correction for diffusion tensor imaging of Rhesus monkey thoracic spinal cord. J Med Primatol 2019; 48:320-328. [PMID: 31148186 DOI: 10.1111/jmp.12422] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 04/03/2019] [Accepted: 05/12/2019] [Indexed: 02/02/2023]
Abstract
BACKGROUND The relatively tiny spinal cord of non-human primate (NHP) causes increased challenge in diffusion tensor imaging (DTI) post-processing. This study aimed to establish a reliable correction strategy applied to clinical DTI images of NHP. METHODS Six normal and partial spinal cord injury (SCI) rhesus monkeys underwent 3T MR scanning. A correction strategy combining multiple iterations and non-rigid deformation was used for DTI image post-processing. Quantitative evaluations were then conducted to investigate effects of distortion correction. RESULTS After correction, longitudinal geometric distortion, global distortion, and residual distance errors were all significantly decreased (P < 0.05). Fractional anisotropy at the injured site was remarkably lower than that at the contralateral site (P = 0.0488) and was substantially lower than those at the adjacent superior (P = 0.0157) and inferior (P = 0.0128) areas at the same side. CONCLUSIONS Our image correction strategy can improve the quality of the DTI images of NHP thoracic cords, contributing to the development of SCI preclinical research.
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Affiliation(s)
- Jia-Sheng Rao
- Beijing Key Laboratory for Biomaterials and Neural Regeneration, Department of Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Zuxiang Liu
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,Innovation Center of Excellence on Brain Science, Chinese Academy of Sciences, Beijing, China.,Department of Biology, College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Can Zhao
- Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China.,Department of Measurement Control and Information Technology, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing, China
| | - Rui-Han Wei
- Beijing Key Laboratory for Biomaterials and Neural Regeneration, Department of Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Ruo-Xi Liu
- Beijing Key Laboratory for Biomaterials and Neural Regeneration, Department of Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Wen Zhao
- Department of Neurobiology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Xia Zhou
- Beijing Key Laboratory for Biomaterials and Neural Regeneration, Department of Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Peng-Yu Tian
- Beijing Key Laboratory for Biomaterials and Neural Regeneration, Department of Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Zhao-Yang Yang
- Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China.,Department of Neurobiology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Xiao-Guang Li
- Beijing Key Laboratory for Biomaterials and Neural Regeneration, Department of Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural Regeneration, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
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36
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Mollink J, Hiemstra M, Miller KL, Huszar IN, Jenkinson M, Raaphorst J, Wiesmann M, Ansorge O, Pallebage-Gamarallage M, van Cappellen van Walsum AM. White matter changes in the perforant path area in patients with amyotrophic lateral sclerosis. Neuropathol Appl Neurobiol 2019; 45:570-585. [PMID: 31002412 PMCID: PMC6852107 DOI: 10.1111/nan.12555] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 04/15/2019] [Indexed: 12/12/2022]
Abstract
OBJECTIVE The aim of this study was to test the hypothesis that white matter degeneration of the perforant path - as part of the Papez circuit - is a key feature of amyotrophic lateral sclerosis (ALS), even in the absence of frontotemporal dementia (FTD) or deposition of pTDP-43 inclusions in hippocampal granule cells. METHODS We used diffusion Magnetic Resonance Imaging (dMRI), polarized light imaging (PLI) and immunohistochemical analysis of post mortem hippocampus specimens from controls (n = 5) and ALS patients (n = 14) to study white matter degeneration in the perforant path. RESULTS diffusion Magnetic Resonance Imaging demonstrated a decrease in fractional anisotropy (P = 0.01) and an increase in mean diffusivity (P = 0.01) in the perforant path in ALS compared to controls. PLI-myelin density was lower in ALS (P = 0.05) and correlated with fractional anisotropy (r = 0.52, P = 0.03). These results were confirmed by immunohistochemistry; both myelin (proteolipid protein, P = 0.03) and neurofilaments (SMI-312, P = 0.02) were lower in ALS. Two out of the fourteen ALS cases showed pTDP-43 pathology in the dentate gyrus, but with comparable myelination levels in the perforant path to other ALS cases. CONCLUSION We conclude that degeneration of the perforant path occurs in ALS patients and that this may occur before, or independent of, pTDP-43 aggregation in the dentate gyrus of the hippocampus. Future research should focus on correlating the degree of cognitive decline to the amount of white matter atrophy in the perforant path.
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Affiliation(s)
- J Mollink
- Department of Anatomy, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands.,Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - M Hiemstra
- Department of Anatomy, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - K L Miller
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - I N Huszar
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - M Jenkinson
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - J Raaphorst
- Department of Neurology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - M Wiesmann
- Department of Anatomy, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - O Ansorge
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - A M van Cappellen van Walsum
- Department of Anatomy, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
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37
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Topgaard D. Diffusion tensor distribution imaging. NMR IN BIOMEDICINE 2019; 32:e4066. [PMID: 30730586 PMCID: PMC6593682 DOI: 10.1002/nbm.4066] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 11/28/2018] [Accepted: 12/19/2018] [Indexed: 05/30/2023]
Abstract
Conventional diffusion MRI yields voxel-averaged parameters that suffer from ambiguities for heterogeneous anisotropic materials such as brain tissue. Using principles from solid-state NMR spectroscopy, we have previously introduced the shape of the diffusion encoding tensor as a separate acquisition dimension that disentangles isotropic and anisotropic contributions to the observed diffusivities, thereby allowing for unconstrained data inversion into diffusion tensor distributions with "size," "shape," and orientation dimensions. Here we combine our recent non-parametric data inversion algorithm and data acquisition protocol with an imaging pulse sequence to demonstrate spatial mapping of diffusion tensor distributions using a previously developed composite phantom with multiple isotropic and anisotropic components. We propose a compact format for visualizing two-dimensional arrays of the distributions, new scalar parameters quantifying intra-voxel heterogeneity, and a binning procedure giving maps of all relevant parameters for each of the components resolved in the multidimensional distribution space.
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Affiliation(s)
- Daniel Topgaard
- Physical Chemistry, Department of ChemistryLund UniversityLundSweden
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38
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Marzi S, Minosse S, Vidiri A, Piludu F, Giannelli M. Diffusional kurtosis imaging in head and neck cancer: On the use of trace-weighted images to estimate indices of non-Gaussian water diffusion. Med Phys 2018; 45:5411-5419. [PMID: 30317646 DOI: 10.1002/mp.13238] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Revised: 10/02/2018] [Accepted: 10/03/2018] [Indexed: 12/11/2022] Open
Abstract
PURPOSE While previous studies have demonstrated the feasibility and potential usefulness of quantitative non-Gaussian diffusional kurtosis imaging (DKI) of the brain, more recent research has focused on oncological application of DKI in various body regions such as prostate, breast, and head and neck (HN). Given the need to minimize scan time during most routine magnetic resonance imaging (MRI) acquisitions of body regions, diffusion-weighted imaging (DWI) with only three orthogonal diffusion weighting directions (x, y, z) is usually performed. Moreover, as water diffusion within malignant tumors is generically thought to be almost isotropic, DWI with only three diffusion weighting directions is considered sufficient for oncological application and it represents the de facto standard in body DKI. In this context, since the kurtosis tensor and diffusion tensor cannot be obtained, the averages of the three directional (Kx , Ky , Kz ) and (Dx , Dy , Dz ) - namely K and D, respectively - represent the best-possible surrogates of directionless DKI-derived indices of kurtosis and diffusivity, respectively. This would require fitting the DKI model to the diffusion-weighted images acquired along each direction (x, y, z) prior to averaging. However, there is a growing tendency to perform only a single fit of the DKI model to the geometric means of the images acquired with diffusion-sensitizing gradient along (x, y, z), referred to as trace-weighted (TW) images. To the best of our knowledge, no in vivo studies have evaluated how TW images affect estimates of DKI-derived indices of K and D. Thus, the aim of this study was to assess the potential bias and error introduced in estimated K and D by fitting the DKI model to the TW images in HN cancer patients. METHODS Eighteen patients with histologically proven malignant tumors of the HN were enrolled in the study. They underwent pretreatment 3 T MRI, including DWI (b-values: 0, 500, 1000, 1500, 2000 s/mm2 ). Some patients had multiple lesions, and thus a total of 34 lesions were analyzed. DKI-derived indices were estimated, voxel-by-voxel, using single diffusion-weighted images along (x, y, z) as well as TW images. A comparison between the two estimation methods was performed by calculating the percentage error in D (Derr ) and K (Kerr ). Also, diffusivity anisotropy (Danis ) and diffusional kurtosis anisotropy (Kanis ) were estimated. Agreements between the two estimation methods were assessed by Bland-Altman plots. The Spearman rank correlation test was used to study the correlations between Kerr /Derr and Danis /Kanis. RESULTS: The median (95% confidence interval) Kerr and Derr were 5.1% (0.8%, 32.6%) and 1.7% (-2.5%, 5.3%), respectively. A significant relationship was observed between Kerr and Danis (correlation coefficient R = 0.694, P < 0.0001), as well as between Kerr and Kanis (R = 0.848, P < 0.0001). CONCLUSIONS In HN cancer, the fit of the DKI model to TW images can introduce bias and error in the estimation of K and D, which may be non-negligible for single lesions, and should hence be adopted with caution.
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Affiliation(s)
- Simona Marzi
- Medical Physics Laboratory, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Silvia Minosse
- Medical Physics Laboratory, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Francesca Piludu
- Radiology and Diagnostic Imaging Department, IRCCS Regina Elena National Cancer Institute, 00144, Rome, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", 56126, Pisa, Italy
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39
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Fedeli L, Belli G, Ciccarone A, Coniglio A, Esposito M, Giannelli M, Mazzoni LN, Nocetti L, Sghedoni R, Tarducci R, Altabella L, Belligotti E, Benelli M, Betti M, Caivano R, Carni' M, Chiappiniello A, Cimolai S, Cretti F, Fulcheri C, Gasperi C, Giacometti M, Levrero F, Lizio D, Maieron M, Marzi S, Mascaro L, Mazzocchi S, Meliado' G, Morzenti S, Noferini L, Oberhofer N, Quattrocchi MG, Ricci A, Taddeucci A, Tenori L, Luchinat C, Gobbi G, Gori C, Busoni S. Dependence of apparent diffusion coefficient measurement on diffusion gradient direction and spatial position - A quality assurance intercomparison study of forty-four scanners for quantitative diffusion-weighted imaging. Phys Med 2018; 55:135-141. [PMID: 30342982 DOI: 10.1016/j.ejmp.2018.09.007] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 09/09/2018] [Accepted: 09/18/2018] [Indexed: 12/15/2022] Open
Abstract
PURPOSE To propose an MRI quality assurance procedure that can be used for routine controls and multi-centre comparison of different MR-scanners for quantitative diffusion-weighted imaging (DWI). MATERIALS AND METHODS 44 MR-scanners with different field strengths (1 T, 1.5 T and 3 T) were included in the study. DWI acquisitions (b-value range 0-1000 s/mm2), with three different orthogonal diffusion gradient directions, were performed for each MR-scanner. All DWI acquisitions were performed by using a standard spherical plastic doped water phantom. Phantom solution ADC value and its dependence with temperature was measured using a DOSY sequence on a 600 MHz NMR spectrometer. Apparent diffusion coefficient (ADC) along each diffusion gradient direction and mean ADC were estimated, both at magnet isocentre and in six different position 50 mm away from isocentre, along positive and negative AP, RL and HF directions. RESULTS A good agreement was found between the nominal and measured mean ADC at isocentre: more than 90% of mean ADC measurements were within 5% from the nominal value, and the highest deviation was 11.3%. Away from isocentre, the effect of the diffusion gradient direction on ADC estimation was larger than 5% in 47% of included scanners and a spatial non uniformity larger than 5% was reported in 13% of centres. CONCLUSION ADC accuracy and spatial uniformity can vary appreciably depending on MR scanner model, sequence implementation (i.e. gradient diffusion direction) and hardware characteristics. The DWI quality assurance protocol proposed in this study can be employed in order to assess the accuracy and spatial uniformity of estimated ADC values, in single- as well as multi-centre studies.
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Affiliation(s)
- Luca Fedeli
- Università degli Studi di Firenze, Firenze, Italy.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Marta Maieron
- A.S.U.I. Udine S. Maria della Misericordia, Udine, Italy
| | | | | | | | | | | | | | | | | | | | | | - Leonardo Tenori
- Magnetic Resonance Center (CERM), Università degli Studi di Firenze, Firenze, Italy
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM), Università degli Studi di Firenze, Firenze, Italy
| | | | - Cesare Gori
- Università degli Studi di Firenze, Firenze, Italy
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40
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Perlbarg V, Lambert J, Butler B, Felfli M, Valabrègue R, Privat AL, Lehéricy S, Petiet A. Alterations of the nigrostriatal pathway in a 6-OHDA rat model of Parkinson's disease evaluated with multimodal MRI. PLoS One 2018; 13:e0202597. [PMID: 30188909 PMCID: PMC6126820 DOI: 10.1371/journal.pone.0202597] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 08/05/2018] [Indexed: 12/13/2022] Open
Abstract
Parkinson's disease is characterized by neurodegeneration of the dopaminergic neurons in the substantia nigra pars compacta. The 6-hydroxydopamine (6-OHDA) rat model has been used to study neurodegeneration in the nigro-striatal dopaminergic system. The goal of this study was to evaluate the reliability of diffusion MRI and resting-state functional MRI biomarkers in monitoring neurodegeneration in the 6-OHDA rat model assessed by quantitative histology. We performed a unilateral injection of 6-OHDA in the striatum of Sprague Dawley rats to produce retrograde degeneration of the dopamine neurons in the substantia nigra pars compacta. We carried out a longitudinal study with a multi-modal approach combining structural and functional MRI together with quantitative histological validation to follow the effects of the lesion. Functional and structural connectivity were assessed in the brain of 6-OHDA rats and sham rats (NaCl injection) at 3 and 6 weeks post-lesioning using resting-state functional MRI and diffusion-weighted. Our results showed (i) increased functional connectivity in ipsi- and contra-lesioned regions of the cortico-basal ganglia network pathway including the motor cortex, the globus pallidus, and the striatum regions at 3 weeks; (ii) increased fractional anisotropy (FA) in the ipsi- and contralateral striatum of the 6-OHDA group at 3 weeks, and increased axial diffusivity (AD) and mean diffusivity in the ipsilateral striatum at 6 weeks; (iii) a trend for increased FA in both substantia nigra of the 6-OHDA group at 3 weeks. Optical density measurements of tyrosine-hydroxylase (TH) staining of the striatum showed good correlations with the FA and AD measurements in the striatum. No correlations were found between the number of TH-stained dopaminergic neurons and MRI measurements in the substantia nigra. This study suggested that (i) FA and AD were reliable biomarkers to evaluate neurodegeneration in the cortico-basal ganglia network of the 6-OHDA model, (ii) diffusion MRI and resting-state functional MRI (rsfMRI) were not sensitive enough to detect changes in the substantia nigra in this model.
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Affiliation(s)
- Vincent Perlbarg
- UPMC / INSERM UMR975, Brain and Spine Institute, Paris, France
- Bioinformatics and Biostatistics Core Facility, Brain and Spine Institute, Paris, France
| | - Justine Lambert
- Center for Neuroimaging Research, Brain and Spine Institute, Paris, France
| | - Benjamin Butler
- Center for Neuroimaging Research, Brain and Spine Institute, Paris, France
| | - Mehdi Felfli
- Center for Neuroimaging Research, Brain and Spine Institute, Paris, France
| | - Romain Valabrègue
- UPMC / INSERM UMR975, Brain and Spine Institute, Paris, France
- Center for Neuroimaging Research, Brain and Spine Institute, Paris, France
| | | | - Stéphane Lehéricy
- UPMC / INSERM UMR975, Brain and Spine Institute, Paris, France
- Center for Neuroimaging Research, Brain and Spine Institute, Paris, France
| | - Alexandra Petiet
- UPMC / INSERM UMR975, Brain and Spine Institute, Paris, France
- Center for Neuroimaging Research, Brain and Spine Institute, Paris, France
- * E-mail:
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De Santis S, Granberg T, Ouellette R, Treaba CA, Herranz E, Mainero C, Toschi N. Early axonal damage in normal appearing white matter in multiple sclerosis: Novel insights from multi-shell diffusion MRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:3024-3027. [PMID: 29060535 DOI: 10.1109/embc.2017.8037494] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Conventional diffusion-weighted MR imaging techniques provide limited specificity in disentangling disease-related microstructural alterations involving changes in both axonal density and myelination. By simultaneously probing multiple diffusion regimens, multi-shell diffusion MRI is capable of increasing specificity to different tissue sub-compartments and hence separate different contributions to changes in diffusion-weighted signal attenuation. Advanced multi-shell diffusion models impose significant requirements on the amount of diffusion weighting (i.e. gradient coil performance) and angular resolution (i.e. in-scanner subject time), which commonly limits their applicability in a clinical setting. In this paper, we apply a high-b-value, high angular resolution multi-shell diffusion MRI protocol to a population of early multiple sclerosis (MS) patients and healthy controls. Through the Composite Hindered and Restricted Model of Diffusion (CHARMED) model, we extract indices for axonal density as well as parameters sensitive to myelin. We demonstrate increased sensitivity to microstructural changes in normal appearing white matter and in lesions in MS as compared to traditional models like DTI. These changes appear to be predominantly in axonal density, pointing towards the existence of axonal damage mechanisms in early MS.
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42
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Mascalchi M, Marzi C, Giannelli M, Ciulli S, Bianchi A, Ginestroni A, Tessa C, Nicolai E, Aiello M, Salvatore E, Soricelli A, Diciotti S. Histogram analysis of DTI-derived indices reveals pontocerebellar degeneration and its progression in SCA2. PLoS One 2018; 13:e0200258. [PMID: 30001379 PMCID: PMC6042729 DOI: 10.1371/journal.pone.0200258] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Accepted: 06/24/2018] [Indexed: 12/16/2022] Open
Abstract
PURPOSE To assess the potential of histogram metrics of diffusion-tensor imaging (DTI)-derived indices in revealing neurodegeneration and its progression in spinocerebellar ataxia type 2 (SCA2). MATERIALS AND METHODS Nine SCA2 patients and 16 age-matched healthy controls, were examined twice (SCA2 patients 3.6±0.7 years and controls 3.3±1.0 years apart) on the same 1.5T scanner by acquiring T1-weighted and diffusion-weighted (b-value = 1000 s/mm2) images. Cerebrum and brainstem-cerebellum regions were segmented using FreeSurfer suite. Histogram analysis of DTI-derived indices, including mean diffusivity (MD), fractional anisotropy (FA), axial (AD) / radial (RD) diffusivity and mode of anisotropy (MO), was performed. RESULTS At baseline, significant differences between SCA2 patients and controls were confined to brainstem-cerebellum. Median values of MD/AD/RD and FA/MO were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (1.11/1.30/1.03×10(-3) mm2/s and 0.14/0.19) than in controls (0.80/1.00/0.70×10(-3) mm2/s and 0.20/0.41). Also, peak location values of MD/AD/RD and FA were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (0.91/1.11/0.81×10(-3) mm2/s and 0.12) than in controls (0.71/0.91/0.63×10(-3) mm2/s and 0.18). Peak height values of FA and MD/AD/RD/MO were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (0.20 and 0.07/0.06/0.07×10(-3) mm2/s/year /0.07) than in controls (0.15 and 0.14/0.11/0.12/×10(-3) mm2/s/year /0.09). The rate of change of MD median values was significantly (p<0.001) higher (i.e., increased) in SCA2 patients (0.010×10(-3) mm2/s/year) than in controls (-0.003×10(-3) mm2/s/year) in the brainstem-cerebellum, whereas no significant difference was found for other indices and in the cerebrum. CONCLUSION Histogram analysis of DTI-derived indices is a relatively straightforward approach which reveals microstructural changes associated with pontocerebellar degeneration in SCA2 and the median value of MD is capable to track its progression.
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Affiliation(s)
- Mario Mascalchi
- “Mario Serio” Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
- * E-mail:
| | - Chiara Marzi
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Cesena, Italy
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital “Azienda Ospedaliero-Universitaria Pisana”, Pisa, Italy
| | - Stefano Ciulli
- “Mario Serio” Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Andrea Bianchi
- “Mario Serio” Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy
| | - Andrea Ginestroni
- Neuroradiology Unit, Azienda Ospedaliero Universitaria Careggi, Florence, Italy
| | - Carlo Tessa
- Department of Radiology and Nuclear Medicine, Versilia Hospital, AUSL 12 Viareggio, Lido di Camaiore (Lu), Italy
| | | | | | - Elena Salvatore
- Department of Neurological Sciences, University of Naples Federico II, Naples, Italy
| | | | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Cesena, Italy
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43
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Kamiya K, Okada N, Sawada K, Watanabe Y, Irie R, Hanaoka S, Suzuki Y, Koike S, Mori H, Kunimatsu A, Hori M, Aoki S, Kasai K, Abe O. Diffusional kurtosis imaging and white matter microstructure modeling in a clinical study of major depressive disorder. NMR IN BIOMEDICINE 2018; 31:e3938. [PMID: 29846988 PMCID: PMC6032871 DOI: 10.1002/nbm.3938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2017] [Revised: 03/13/2018] [Accepted: 04/05/2018] [Indexed: 05/13/2023]
Abstract
Major depressive disorder (MDD) is a globally prevalent psychiatric disorder that results from disruption of multiple neural circuits involved in emotional regulation. Although previous studies using diffusion tensor imaging (DTI) found smaller values of fractional anisotropy (FA) in the white matter, predominantly in the frontal lobe, of patients with MDD, studies using diffusion kurtosis imaging (DKI) are scarce. Here, we used DKI whole-brain analysis with tract-based spatial statistics (TBSS) to investigate the brain microstructural abnormalities in MDD. Twenty-six patients with MDD and 42 age- and sex-matched control subjects were enrolled. To investigate the microstructural pathology underlying the observations in DKI, a compartment model analysis was conducted focusing on the corpus callosum. In TBSS, the patients with MDD showed significantly smaller values of FA in the genu and frontal portion of the body of the corpus callosum. The patients also had smaller values of mean kurtosis (MK) and radial kurtosis (RK), but MK and RK abnormalities were distributed more widely compared with FA, predominantly in the frontal lobe but also in the parietal, occipital, and temporal lobes. Within the callosum, the regions with smaller MK and RK were located more posteriorly than the region with smaller FA. Model analysis suggested significantly smaller values of intra-neurite signal fraction in the body of the callosum and greater fiber dispersion in the genu, which were compatible with the existing literature of white matter pathology in MDD. Our results show that DKI is capable of demonstrating microstructural alterations in the brains of patients with MDD that cannot be fully depicted by conventional DTI. Though the issues of model validation and parameter estimation still remain, it is suggested that diffusion MRI combined with a biophysical model is a promising approach for investigation of the pathophysiology of MDD.
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Affiliation(s)
- Kouhei Kamiya
- Department of RadiologyThe University of TokyoTokyoJapan
- Department of RadiologyJuntendo University School of MedicineTokyoJapan
| | - Naohiro Okada
- Department of NeuropsychiatryThe University of TokyoTokyoJapan
| | - Kingo Sawada
- Department of NeuropsychiatryThe University of TokyoTokyoJapan
| | | | - Ryusuke Irie
- Department of RadiologyThe University of TokyoTokyoJapan
- Department of RadiologyJuntendo University School of MedicineTokyoJapan
| | | | - Yuichi Suzuki
- Department of RadiologyThe University of Tokyo HospitalTokyoJapan
| | - Shinsuke Koike
- Department of NeuropsychiatryThe University of TokyoTokyoJapan
| | - Harushi Mori
- Department of RadiologyThe University of TokyoTokyoJapan
| | - Akira Kunimatsu
- Department of RadiologyIMSUT (The Institute of Medical Science, The University of Tokyo) HospitalTokyoJapan
| | - Masaaki Hori
- Department of RadiologyJuntendo University School of MedicineTokyoJapan
| | - Shigeki Aoki
- Department of RadiologyJuntendo University School of MedicineTokyoJapan
| | - Kiyoto Kasai
- Department of NeuropsychiatryThe University of TokyoTokyoJapan
| | - Osamu Abe
- Department of RadiologyThe University of TokyoTokyoJapan
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44
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Pozorski V, Oh JM, Adluru N, Merluzzi AP, Theisen F, Okonkwo O, Barzgari A, Krislov S, Sojkova J, Bendlin BB, Johnson SC, Alexander AL, Gallagher CL. Longitudinal white matter microstructural change in Parkinson's disease. Hum Brain Mapp 2018; 39:4150-4161. [PMID: 29952102 DOI: 10.1002/hbm.24239] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 05/06/2018] [Accepted: 05/22/2018] [Indexed: 01/06/2023] Open
Abstract
Postmortem studies of Parkinson's disease (PD) suggest that Lewy body pathology accumulates in a predictable topographical sequence, beginning in the olfactory bulb, followed by caudal brainstem, substantia nigra, limbic cortex, and neocortex. Diffusion-weighted imaging (DWI) is sensitive, if not specific, to early disease-related white matter (WM) change in a variety of traumatic and degenerative brain diseases. Although numerous cross-sectional studies have reported DWI differences in cerebral WM in PD, only a few longitudinal studies have investigated whether DWI change exceeds that of normal aging or coincides with regional Lewy body accumulation. This study mapped regional differences in the rate of DWI-based microstructural change between 29 PD patients and 43 age-matched controls over 18 months. Iterative within- and between-subject tensor-based registration was completed on motion- and eddy current-corrected DWI images, then baseline versus follow-up difference maps of fractional anisotropy, mean, radial, and axial diffusivity were analyzed in the Biological Parametric Mapping toolbox for MATLAB. This analysis showed that PD patients had a greater decline in WM integrity in the rostral brainstem, caudal subcortical WM, and cerebellar peduncles, compared with controls. In addition, patients with unilateral clinical signs at baseline experienced a greater rate of WM change over the 18-month study than patients with bilateral signs. These findings suggest that rate of WM microstructural change in PD exceeds that of normal aging and is maximal during early stage disease. In addition, the neuroanatomic locations (rostral brainstem and subcortical WM) of accelerated WM change fit with current theories of topographic disease progression.
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Affiliation(s)
- Vincent Pozorski
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin.,Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Jennifer M Oh
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin.,Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Nagesh Adluru
- Waisman Laboratory for Brain Imaging and Behavior, Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin
| | - Andrew P Merluzzi
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Frances Theisen
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin.,Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Ozioma Okonkwo
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin.,Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Amy Barzgari
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin.,Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Stephanie Krislov
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin.,Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Jitka Sojkova
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Barbara B Bendlin
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin.,Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Sterling C Johnson
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin.,Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Andrew L Alexander
- Waisman Laboratory for Brain Imaging and Behavior, Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin.,Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Catherine L Gallagher
- William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin.,Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.,Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
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45
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Mascalchi M, Salvadori E, Toschi N, Giannelli M, Orsolini S, Ciulli S, Ginestroni A, Poggesi A, Giorgio A, Lorenzini F, Pasi M, De Stefano N, Pantoni L, Inzitari D, Diciotti S. DTI-derived indexes of brain WM correlate with cognitive performance in vascular MCI and small-vessel disease. A TBSS study. Brain Imaging Behav 2018; 13:594-602. [DOI: 10.1007/s11682-018-9873-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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46
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López-Sánchez EJ, Romero JM, Yépez-Martínez H. Fractional cable equation for general geometry: A model of axons with swellings and anomalous diffusion. Phys Rev E 2018; 96:032411. [PMID: 29346980 DOI: 10.1103/physreve.96.032411] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Indexed: 11/07/2022]
Abstract
Different experimental studies have reported anomalous diffusion in brain tissues and notably this anomalous diffusion is expressed through fractional derivatives. Axons are important to understand neurodegenerative diseases such as multiple sclerosis, Alzheimer's disease, and Parkinson's disease. Indeed, abnormal accumulation of proteins and organelles in axons is a hallmark of these diseases. The diffusion in the axons can become anomalous as a result of this abnormality. In this case the voltage propagation in axons is affected. Another hallmark of different neurodegenerative diseases is given by discrete swellings along the axon. In order to model the voltage propagation in axons with anomalous diffusion and swellings, in this paper we propose a fractional cable equation for a general geometry. This generalized equation depends on fractional parameters and geometric quantities such as the curvature and torsion of the cable. For a cable with a constant radius we show that the voltage decreases when the fractional effect increases. In cables with swellings we find that when the fractional effect or the swelling radius increases, the voltage decreases. Similar behavior is obtained when the number of swellings and the fractional effect increase. Moreover, we find that when the radius swelling (or the number of swellings) and the fractional effect increase at the same time, the voltage dramatically decreases.
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Affiliation(s)
- Erick J López-Sánchez
- Posgrado en Ciencias Naturales e Ingeniería, Universidad Autónoma Metropolitana, Cuajimalpa and Vasco de Quiroga 4871, Santa Fe Cuajimalpa, Ciudad de México 05300, Mexico
| | - Juan M Romero
- Departamento de Matemáticas Aplicadas y Sistemas, Universidad Autónoma Metropolitana-Cuajimalpa, Vasco de Quiroga 4871, Santa Fe Cuajimalpa, Ciudad de México 05300, Mexico
| | - Huitzilin Yépez-Martínez
- Universidad Autónoma de la Ciudad de México, Prolongación San Isidro 151, San Lorenzo Tezonco, Iztapalapa, Ciudad de México 09790, Mexico
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47
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Météreau E, Beaudoin-Gobert M, Duperrier S, Thobois S, Tremblay L, Sgambato-Faure V. Diffusion tensor imaging marks dopaminergic and serotonergic lesions in the Parkinsonian monkey. Mov Disord 2017; 33:298-309. [DOI: 10.1002/mds.27201] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 08/24/2017] [Accepted: 08/27/2017] [Indexed: 12/31/2022] Open
Affiliation(s)
- Elise Météreau
- Université de Lyon, Centre National de la Recherche Scientifique, Institut des Sciences Cognitives Marc Jeannerod; Bron France
| | - Maude Beaudoin-Gobert
- Université de Lyon, Centre National de la Recherche Scientifique, Institut des Sciences Cognitives Marc Jeannerod; Bron France
| | - Sandra Duperrier
- Université de Lyon, Centre National de la Recherche Scientifique, Institut des Sciences Cognitives Marc Jeannerod; Bron France
| | - Stéphane Thobois
- Université de Lyon, Centre National de la Recherche Scientifique, Institut des Sciences Cognitives Marc Jeannerod; Bron France
- Hospices Civils de Lyon, Hôpital Neurologique Pierre Wertheimer; Lyon France
| | - Léon Tremblay
- Université de Lyon, Centre National de la Recherche Scientifique, Institut des Sciences Cognitives Marc Jeannerod; Bron France
| | - Véronique Sgambato-Faure
- Université de Lyon, Centre National de la Recherche Scientifique, Institut des Sciences Cognitives Marc Jeannerod; Bron France
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48
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Farrar DC, Mian AZ, Budson AE, Moss MB, Koo BB, Killiany RJ. Retained executive abilities in mild cognitive impairment are associated with increased white matter network connectivity. Eur Radiol 2017; 28:340-347. [PMID: 28695358 DOI: 10.1007/s00330-017-4951-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 06/07/2017] [Accepted: 06/15/2017] [Indexed: 02/05/2023]
Abstract
PURPOSE To describe structural network differences in individuals with mild cognitive impairment (MCI) with high versus low executive abilities, as reflected by measures of white matter connectivity using diffusion tensor imaging (DTI). MATERIALS AND METHODS This was a retrospective, cross-sectional study. Of the 128 participants from the Alzheimer's Disease Neuroimaging Initiative database who had both a DTI scan as well as a diagnosis of MCI, we used an executive function score to classify the top 15 scoring patients as high executive ability, and the bottom-scoring 16 patients as low executive ability. Using a regions-of-interest-based analysis, we constructed networks and calculated graph theory measures on the constructed networks. We used automated tractography in order to compare differences in major white matter tracts. RESULTS The high executive ability group yielded greater network size, density and clustering coefficient. The high executive ability group reflected greater fractional anisotropy bilaterally in the inferior and superior longitudinal fasciculi. CONCLUSIONS The network measures of the high executive ability group demonstrated greater white matter integrity. This suggests that white matter reserve may confer greater protection of executive abilities. Loss of this reserve may lead to greater impairment in the progression to Alzheimer's disease dementia. KEY POINTS • The MCI high executive ability group yielded a larger network. • The MCI high executive ability group had greater FA in numerous tracts. • White matter reserve may confer greater protection of executive abilities. • Loss of executive reserve may lead to greater impairment in AD dementia.
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Affiliation(s)
- Danielle C Farrar
- Department of Anatomy and Neurobiology, Boston University School of Medicine, 650 Albany St, Basement, Boston, MA, 02118, USA.
| | - Asim Z Mian
- Department of Radiology, Boston University School of Medicine, Boston, MA, USA
| | | | - Mark B Moss
- Department of Anatomy and Neurobiology, Boston University School of Medicine, 650 Albany St, Basement, Boston, MA, 02118, USA
| | - Bang Bon Koo
- Department of Anatomy and Neurobiology, Boston University School of Medicine, 650 Albany St, Basement, Boston, MA, 02118, USA
| | - Ronald J Killiany
- Department of Anatomy and Neurobiology, Boston University School of Medicine, 650 Albany St, Basement, Boston, MA, 02118, USA
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49
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Weber RA, Chan CH, Nie X, Maggioncalda E, Valiulis G, Lauer A, Hui ES, Jensen JH, Adkins DL. Sensitivity of diffusion MRI to perilesional reactive astrogliosis in focal ischemia. NMR IN BIOMEDICINE 2017; 30:10.1002/nbm.3717. [PMID: 28272771 PMCID: PMC5759343 DOI: 10.1002/nbm.3717] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2016] [Revised: 01/04/2017] [Accepted: 01/31/2017] [Indexed: 06/06/2023]
Abstract
Reactive astrogliosis is a response to injury in the central nervous system that plays an essential role in inflammation and tissue repair. It is characterized by hypertrophy of astrocytes, alterations in astrocyte gene expression and astrocyte proliferation. Reactive astrogliosis occurs in multiple neuropathologies, including stroke, traumatic brain injury and Alzheimer's disease, and it has been proposed as a possible source of the changes in diffusion magnetic resonance imaging (dMRI) metrics observed with these diseases. In this study, the sensitivity of dMRI to reactive astrogliosis was tested in an animal model of focal acute and subacute ischemia induced by the vasoconstricting peptide, endothelin-1. Reactive astrogliosis in perilesional cortex was quantified by calculating the astrocyte surface density as determined with a glial fibrillary acidic protein (GFAP) antibody, whereas perilesional diffusion changes were measured in vivo with diffusional kurtosis imaging. We found substantial changes in the surface density of GFAP-positive astrocyte processes and modest changes in dMRI metrics in the perilesional motor cortex following stroke. Although there are time point-specific correlations between dMRI and histological measures, there is no definitive evidence for a causal relationship.
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Affiliation(s)
- Rachel A. Weber
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Clifford H. Chan
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Xingju Nie
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Emily Maggioncalda
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Grace Valiulis
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Abigail Lauer
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Edward S. Hui
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Jens H. Jensen
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
| | - DeAnna L. Adkins
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, USA
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA
- Department of Health Science and Research, Medical University of South Carolina, Charleston, South Carolina, USA
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Duggento A, Giannelli M, Tessa C, Lanzafame S, Guerrisi M, Toschi N. Distribution-aware estimation of the minimum achievable uncertainty in diffusion-tensor imaging (DTI). ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:5541-5544. [PMID: 28269512 DOI: 10.1109/embc.2016.7591982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Diffusion tensor imaging (DTI) provides exquisite sensitivity to structural and microstructural characteristics of brain tissue, and is routinely employed in advanced neuroimaging applications. DTI is commonly performed using intrinsically noisy echo-planar imaging techniques and poses high demands both on scanner performance and on in-scanner subject time, which in turn is directly related to the number of diffusion-weighting direction one requires. While DTI-derived indices such as fractional anisotropy (FA), diffusion tensor trace and anisotropy mode have proven extremely useful in characterizing disease-related aberrations, their estimation is commonly performed using fitting routines that do not properly take into account MRI noise distribution. In this paper, we present a distribution-aware maximum likelihood tensor estimation framework which also allows, for the first time, separate local noise estimation in both diffusion weighted and reference images. We validate our framework using multiple water phantom diffusion weighted acquisitions, and demonstrate its feasibility in human data. We then employ our framework within Monte Carlo simulations to show how the minimum achievable uncertainty attainable in DTI depends on signal-to-noise ratio (SNR) and number of diffusion gradient directions, demonstrating that these dependencies could be recast into simple power laws which may serve as guidelines for application-specific DTI protocol design.
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